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+ if (r1norm > 0.6 || rmaxnorm > 0.6){ + return false; + } + return true; + ``` + Implementación de la decisión sonoro/sordo (voiced/unvoiced): + La señal se considera sonora (false) si la correlación de primer orden (r1norm) o la correlación en el máximo secundario (rmaxnorm) superan el umbral de 0.6. En caso contrario, se considera sorda (true). + * Puede serle útil seguir las instrucciones contenidas en el documento adjunto `código.pdf`. - Una vez completados los puntos anteriores, dispondrá de una primera versión del estimador de pitch. El resto del trabajo consiste, básicamente, en obtener las mejores prestaciones posibles con él. - * Utilice el programa `wavesurfer` para analizar las condiciones apropiadas para determinar si un - segmento es sonoro o sordo. + * Utilice el programa `wavesurfer` para analizar las condiciones apropiadas para determinar si un segmento es sonoro o sordo. - - Inserte una gráfica con la estimación de pitch incorporada a `wavesurfer` y, junto a ella, los - principales candidatos para determinar la sonoridad de la voz: el nivel de potencia de la señal - (r[0]), la autocorrelación normalizada de uno (r1norm = r[1] / r[0]) y el valor de la - autocorrelación en su máximo secundario (rmaxnorm = r[lag] / r[0]). + - Inserte una gráfica con la estimación de pitch incorporada a `wavesurfer` y, junto a ella, los principales candidatos para determinar la sonoridad de la voz: el nivel de potencia de la señal (r[0]), la autocorrelación normalizada de uno (r1norm = r[1] / r[0]) y el valor de la autocorrelación en su máximo secundario (rmaxnorm = r[lag] / r[0]). Puede considerar, también, la conveniencia de usar la tasa de cruces por cero. - Recuerde configurar los paneles de datos para que el desplazamiento de ventana sea el adecuado, que - en esta práctica es de 15 ms. + Recuerde configurar los paneles de datos para que el desplazamiento de ventana sea el adecuado, que en esta práctica es de 15 ms. - - Use el estimador de pitch implementado en el programa `wavesurfer` en una señal de prueba y compare - su resultado con el obtenido por la mejor versión de su propio sistema. Inserte una gráfica - ilustrativa del resultado de ambos estimadores. + - Use el estimador de pitch implementado en el programa `wavesurfer` en una señal de prueba y compare su resultado con el obtenido por la mejor versión de su propio sistema. Inserte una gráfica ilustrativa del resultado de ambos estimadores. - Aunque puede usar el propio Wavesurfer para obtener la representación, se valorará - el uso de alternativas de mayor calidad (particularmente Python). + Aunque puede usar el propio Wavesurfer para obtener la representación, se valorará el uso de alternativas de mayor calidad (particularmente Python). + ![Captura del Docopt](img/grafica_completa_sonoritat.png) + * Optimice los parámetros de su sistema de estimación de pitch e inserte una tabla con las tasas de error y el *score* TOTAL proporcionados por `pitch_evaluate` en la evaluación de la base de datos `pitch_db/train`.. +## Optimización de la estimación de pitch + + - Para maximizar la precisión del estimador de pitch, hemos ajustado los umbrales de decisión sonor/sord (unvoiced) a los valores óptimos de -52 dB para la potencia y 0.6 para las correlaciones (Correlación al primer desplazamiento (r1norm) y máximo de la autocorrelación secundaria (rmaxnorm)), además de implementar la ventana de Hamming. Este ha sido el código que hemos añadido para implementar la ventana de Hamming: + + ```cpp + for (unsigned int n = 0; n < frameLen; ++n) { + window[n] = 0.54 - 0.46 * cos(2 * M_PI * n / (frameLen - 1)); + } + ``` + + Además, originalmente el sistema solo evaluaba la periodicidad mediante la autocorrelación. Hemos mejorado esto añadiendo un umbral de potencia que actúa como filtro previo para eliminar el ruido de fondo. Al descartar los fragmentos con baja energía antes de analizar la autocorrelación, hemos conseguido eliminar prácticamente todos los falsos positivos en las zonas de silencio o ruido. + + También hemos cambiado la lógica cuando miramos la autocorrelación para detectar si es sordo o sonoro, ya que haciéndolo de la forma de antes (si el señal superaba el umbral se le asignaba como señal sonoro) era mucho más permisivo que haciéndolo al revés (si el señal no supera el umbral se asigna como sordo). + + Esta nueva forma es mucho más robusta porque, para que un frame sea detectado como sonoro, ahora debe cumplir todas las condiciones simultáneamente (energía suficiente y alta periodicidad en ambos parámetros). + + Con estos cambios hemos pasado de un 64% a un 90.5%. + + Nueva regla de decisión: + + ```cpp + if (pot < llindar_pot) { + return true; + } + + if (r1norm < llindar_r1norm || rmaxnorm < llindar_rmaxnorm) { + return true; + } + return false; + ``` + + + Tabla con la tasa de error y el *score* TOTAL: + + **Num. frames: 11200 = 7045 unvoiced + 4155 voiced** + + | Métrica | Resultado | + | :--- | :--- | + | Unvoiced frames as voiced | 303/7045 (4.30 %) | + | Voiced frames as unvoiced | 442/4155 (10.64 %) | + | Gross voiced errors (+20.00 %) | 82/3713 (2.21 %) | + | MSE of fine errors | 2.05 % | + | **TOTAL SCORE** | **90.50 %** | + + ### Parámetros finales utilizados: + * **Umbral de potencia (`-p`):** -49 dB + * **Umbral de rmaxnorm (`-M`):** 0.36 + * **Umbral de r1norm (`-1`):** 0.36 + * **Ventana:** Hamming + + + El porcentaje de Gross Errors es bastante bajo (2.21%) y el del MSE también (2.05%). Esto demuestran que el algoritmo es muy preciso y fiable cuando detecta la presencia de voz. Los errores de octava son mínimos. El error principal está en los Voiced frames as unvoiced (10.64%). Esto indica que el sistema tiende a ser conservador y etiqueta como sordos (f0=0) algunos segmentos que contienen voz, probablemente en zonas de baja energía o transiciones. + + Ejercicios de ampliación ------------------------ @@ -76,10 +158,8 @@ Ejercicios de ampliación * Técnicas de preprocesado: filtrado paso bajo, diezmado, *center clipping*, etc. * Técnicas de postprocesado: filtro de mediana, *dynamic time warping*, etc. - * Métodos alternativos a la autocorrelación: procesado cepstral, *average magnitude difference function* - (AMDF), etc. - * Optimización **demostrable** de los parámetros que gobiernan el estimador, en concreto, de los que - gobiernan la decisión sonoro/sordo. + * Métodos alternativos a la autocorrelación: procesado cepstral, *average magnitude difference function* (AMDF), etc. + * Optimización **demostrable** de los parámetros que gobiernan el estimador, en concreto, de los que gobiernan la decisión sonoro/sordo. * Cualquier otra técnica que se le pueda ocurrir o encuentre en la literatura. Encontrará más información acerca de estas técnicas en las [Transparencias del Curso](https://atenea.upc.edu/pluginfile.php/2908770/mod_resource/content/3/2b_PS%20Techniques.pdf) @@ -92,7 +172,272 @@ Ejercicios de ampliación También se valorará la realización de un estudio de los parámetros involucrados. Por ejemplo, si se opta por implementar el filtro de mediana, se valorará el análisis de los resultados obtenidos en función de la longitud del filtro. - + + ## MEJORAS IMPLEMENTADAS + Les millores mencionades a continuació han estat implementades de forma iterativa, és a dir, s'han anat afegint una a una i comprovant el seu efecte sobre el score total. + + ### Afegir ZCR com a nou paràmetre + La primera millora probada ha estat afegir el parametre de zcr per poder evaluar millor si és tracta d'un so sonor o bé sord, ja que si la zcr és alta voldrà dir que es sord. + + Per tant s'ha modificat el programa per considerar un nou llindar anomenat llindar_zcr, tant al codi com al docopt, que se li ha atribuit un valor de 0.25 de default. A més a més cal tenir en compte que per poder evaluar diferents valors, s'ha hagut de : + + * Afegir el "$@" a scripts/run_get_pitch.sh, línia 13, dins de la comanda que crida get_pitch: + ```cpp + $GETF0 "$@" $fwav $ff0 > /dev/null + ``` + * Per què cal "$@"? + "$@" representa tots els arguments extra que li passes al script (--zcr 0.25, etc.). Sense això, el script executa get_pitch ignorant-los, i sempre usa els valors per defecte. + + * Així, quan fas ./run_get_pitch.sh --zcr 0.25, el --zcr 0.25 es passa literalment a get_pitch abans dels fitxers d'entrada/sortida. + + Resultats després de fer run_get_pitch: + ```cpp + ### Summary + Num. frames: 11200 = 7045 unvoiced + 4155 voiced + Unvoiced frames as voiced: 271/7045 (3.85 %) + Voiced frames as unvoiced: 459/4155 (11.05 %) + Gross voiced errors (+20.00 %): 81/3487 (2.19 %) + MSE of fine errors: 2.03 % + + ===> TOTAL: 90.64 % + -------------------------- + ``` + + El seu efecte és petit perquè el pitch es mesura amb autocorrelació, i el ZCR només ajuda a la decisió sonor/sord (si el frame té pitch o no). Dona +0.3% de score, però per millorar l'estimació cal tenir en compte l'estimació directament. + + ### Docopt + ![Captura del Docopt](img/Docopt.png) + + L'exemple d'us és el següent, on s'ha activat el cepstrum i s'han ajustat els llindars de zcr, potència i correlació per veure com afecta al score total: + + ```cpp + run_get_pitch -c -z 0.10 --pot=-52 -1 0.23 -M 0.27 + ``` + + ### Preprocessat i Postprocessat + + #### Preprocessat amb filtre pas baix: + Després s'ha implementat una etapa de preprocessat que consisteix en un filtre de pas baix amb una finestra de 5 mostres. Aquest filtre s'aplica al senyal original de forma completa abans de la divisió en trames (frames). + + L'objectiu principal d'aquest filtre és atenuar les components d'alta freqüència i el soroll de quantificació que poden interferir en el càlcul de l'autocorrelació. En suavitzar la forma d'ona, s'aconsegueix que els pics de la funció d'autocorrelació siguin més clars i definits, reduint els errors on es confon la veu amb soroll (Voiced as Unvoiced). + + El codi que hem implementat per dur a terme el preprocessat ha estat el següent: + + ```cpp + if (x.size() > 5) { + vector x_filtered = x; + int navg = 5; // Tamaño de la ventana del filtro + + for (size_t i = navg; i < x.size(); ++i) { + float sum = 0.0; + for (int j = 0; j < navg; ++j) { + sum += x[i - j]; + } + x_filtered[i] = sum / navg; + } + x = x_filtered; // Sustituimos la señal original por la filtrada + } + ``` + + + #### Postprocessat amb filtre de mediana: + Un cop obtingut el vector de freqüències fonamentals estimades, hem aplicat un filtre de mediana de longitud 3. Aquest pas de postprocesat és fonamental per eliminar els anomenats spikes o errors puntuals de l'estimador. El filtre de mediana és molt eficaç eliminant les decisions errònies de sonoritat en trames de transició, ja que ignora els valors atípics (outliers) sense suavitzar excessivament els contorns d'entonació reals. La tria d'una finestra de 3 mostres permet corregir errors aïllats sense introduir un retard significatiu ni perdre la variabilitat natural de la veu. + + El codi que hem implementat per dur a terme el preprocessat ha estat el següent: + + ```cpp + vector f0_filtered = f0; // Copia para no modificar mientras leemos + + for (size_t i = 1; i < f0.size() - 1; ++i) { + // Tenemos que crear un vector con tres valores para poder hacer la mediana --> valor anterior, el actual y el siguiente + vector vectdemediana = {f0[i-1], f0[i], f0[i+1]}; + + // Ordenamos los 3 valores + sort(vectdemediana.begin(), vectdemediana.end()); + + // El valor del medio (índice 1) es la mediana + f0_filtered[i] = vectdemediana[1]; + } + f0 = f0_filtered; // Actualizamos el vector original + ``` + + Els resultat d'utilitzar aquestes tècniques ens donen el següent resultat quan estimem el pitch. + ```cpp + ### Summary + Num. frames: 11200 = 7045 unvoiced + 4155 voiced + Unvoiced frames as voiced: 357/7045 (5.07 %) + Voiced frames as unvoiced: 284/4155 (6.84 %) + Gross voiced errors (+20.00 %): 75/3487 (1.94 %) + MSE of fine errors: 2.68 % + + ===> TOTAL: 91.00 % + -------------------------- + ``` + + + #### Conclusions de l'Impacte del Pre i Post-processat + + L'anàlisi comparativa entre el sistema base amb ZCR (90.64%) i el sistema complet amb preprocessat i postprocessat (91.00%) permet extreure les següents conclusions: + + * **Millora en la detecció de sonoritat:** s'ha reduït l'error de trames sonores perdudes (*Voiced as unvoiced*) en un **4.21%**, demostrant que és eficaç per ressaltar la periodicitat en segments de veu febles. + * **Correcció d'errors grossos:** s'ha reduir els *Gross Errors* per sota del 2%, eliminant pics de freqüència aïllats. + * **Balanç Final:** Tot i un lleuger augment en la classificació de soroll com a veu, el sistema presenta un comportament molt més estable i una precisió global superior, assolint el **91.00%**. + + + ### Cepstrum i Autocorrelació + Hem usat el cepstrum per determinar sobre quinens mostres estaria el nostre pitch i d'allà calculem l'autocorrelació al voltnat d'aquelles mostres per tenir una cerca del pitch computacionalment més bona atés que calcular l'autocorrelació és més car, per tant quant menys mostres usem millor. En aquest cas, no hem tingut en compte ni el preprocessat ni el postprocessat. + + Per no fer el canvi de forma permanent, s'ha creat una variable al docopt de forma boolean perqué l'usuari pogui escollir si desitja operar amb el cepstrum o amb l'autocorrelació. La variable usada ha estat: + *-c, --activar_ceps Activa el càlcul del Cepstrum per trobar el pitch [Default: false]* + + + **A) Càlcul dels indexs del cepstrum** + + Per fer-ho, hem fet la funció cepstrum, la qual ha fet us de la llibreria FFT de Fastest Fourier Transform in the West (FFTW) per calcular la FFT i la IFFT. Aquesta funció segueix els passos següents: + - Zero Padding (cal fer-la amb mida potència de 2, tipus 2^ceil(log2(N))) + - |X| = sqrt(real^2 + imag^2) + - log(|X| + epsilon) + - IFFT del log-espectre + - c[n] = part real de la IFFT + + + + ```cpp + void PitchAnalyzer::cepstrum(const vector &x, vector &c) const { + + // Assegurar que N cobreix fins a lag=320 sense fer aliasing (N > 2*npitch_max) + unsigned int N = 1024; + while (N < x.size() * 2) N <<= 1; + ffft::FFTReal fft(N); + // 1. Pre-èmfasi i Zero-padding + vector buf(N, 0.0f); + buf[0] = x[0]; + for(unsigned int i = 1; i < x.size(); ++i) { + buf[i] = x[i] - 0.97f * x[i-1]; // Filtre de pre-èmfasi + } + + // 2. FFT + vector spec(N); + fft.do_fft(spec.data(), buf.data()); + + // 3. Log-magnitud (format packed) + vector logmag(N/2 + 1); + for (unsigned int k = 0; k <= N/2; ++k) { + float re = spec[k]; + float im = (k == 0 || k == N/2) ? 0.0f : spec[N/2 + k]; + logmag[k] = log(sqrt(re*re + im*im) + 1e-10f); + } + + // 4. Omplir part real, imag = 0 + fill(spec.begin(), spec.end(), 0.0f); + copy(logmag.begin(), logmag.end(), spec.begin()); + + // 5. IFFT → cepstrum + fft.do_ifft(spec.data(), buf.data()); + fft.rescale(buf.data()); + + // 6. Copiar a c + for (unsigned int i = 0; i < c.size(); ++i) + c[i] = buf[i]; + } + ``` + **B) Càlcul del pic del cesptrum o l'autocorrelació** + + Per estimar el segon pic del cepstrum o l'autocorrelació hem fet ús del codi mencionat abaix, a més a més s'ha de tenir en compte que s'ha fet la funció perqué depenent de si l'activar_ceps està activa calculi el pitch a partir de la funció del cepstrum i en cas de que no ho estigui faci us de l'autocorrelació directament: + + ```cpp + bool usar_cepstrum = activar_ceps; + iter = usar_cepstrum ? c.begin() : r.begin(); + for(iR= iRMax = iter + npitch_min ; iR < iter + npitch_max ; iR++){ + if (*iR > *iRMax){ + iRMax =iR; + } } + + unsigned int lag = iRMax - iter; + ``` + + **C) Càlcul del pitch desde el segon pic secundari** + + Hem fet ús de la funció de cepstrum per trobar el pitch, seguint els següents passos: + * Calcular el cepstrum del frame amb la funció cepstrum mencionada en l'apartat A). + * Localitzar el màxim secundari del cepstrum entre les posicions corresponents a 50 Hz i 500 Hz (lag entre 160 i 320). + * Calcular rmaxnorm = c[lag] / c[0] i r1norm = c[1] / c[0]. + * Aplicar la regla de decisió sonor/sord amb els llindars corresponents tenint en compte el nou pitch calculat (lag). + + + ```cpp + // Si hem usat el cepstrum, el pic d'autocorrelació pot estar lleugerament desplaçat. + // Busquem el màxim local de l'autocorrelació al voltant del lag trobat. + float r_max_val = r[lag]; + if (activar_ceps) { + int search_range = 3; // Marge de cerca + for (int k = -search_range; k <= search_range; ++k) { + int current_lag = lag + k; + if (current_lag >= 0 && current_lag < (int)r.size()) { + if (r[current_lag] > r_max_val) { + r_max_val = r[current_lag]; + } } } } + ``` + + **Resultats després de fer run_get_pitch -c:** + ```cpp + ### Summary + Num. frames: 11200 = 7045 unvoiced + 4155 voiced + Unvoiced frames as voiced: 165/7045 (2.34 %) + Voiced frames as unvoiced: 668/4155 (16.08 %) + Gross voiced errors (+20.00 %): 32/3487 (0.92 %) + MSE of fine errors: 2.33 % + + ===> TOTAL: 89.47 % + -------------------------- + ``` + El resultat ha empitjorat envers al que teniem. + + + ### Optimització de paràmetres + Després de provar diferents combinacions de paràmetres i tècniques, hem arribat a la següent configuració final, que ens ha proporcionat un score del 91.63%: + Hem fet us dels scripts : **grid_search.sh** i **grid_search_fine.sh** per provar diferents combinacions de paràmetres i trobar la millor configuració. + + Com a resultat d'aquest procés, hem obtingut la següent configuració final: + + * **Umbral de potencia (`-p`):** -50 dB + * **Umbral de rmaxnorm (`-M`):** 0.34 + * **Umbral de r1norm (`-1`):** 0.23 + * **Ventana:** Hamming + * **Umbral de zcr (`-z`):** 0.10 + * **Activar cepstrum (`-c`):** true + + Tabla con la tasa de error y el *score* TOTAL: + + **Num. frames: 11200 = 7045 unvoiced + 4155 voiced** + + | Métrica | Resultado | + | :--- | :--- | + | Unvoiced frames as voiced | 285/7045 (4.05 %) | + | Voiced frames as unvoiced | 280/4155 (6.74 %) | + | Gross voiced errors (+20.00 %) | 84/3875 (2.17 %) | + | MSE of fine errors | 2.70 % | + | **TOTAL SCORE** | **91.63 %** | + + Aquesta configuració ha estat obtinguda després d'un procés iteratiu d'ajust dels paràmetres, l'addició de tècniques de preprocesat (filtre pas baix) i postprocesat (filtre de mediana) i afegint el processat del cepstrum. + + + +## Conclusions Finals + +Un cop finalitzat el procés d'optimització i integració de les diferents tècniques, es poden extreure les següents conclusions sobre el desenvolupament del detector de pitch: + +* **Impacte de la Robustesa:** El fet d'aconseguir el 91.63% d'èxit és el resultat directe de combinar anàlisi temporal (autocorrelació) i espectral (Cepstrum). Mentre que el Cepstrum per si sol presentava una taxa de trames sonores perdudes elevada (16.08%), utilitzar-lo com a guia per a l'autocorrelació ha permès millorar la detecció de silenci, baixant el soroll confós amb veu (*Unvoiced as voiced*) a un mínim del 4.05%. + +* **Pre i Post-processat:** S'ha demostrat que és igual d'important el tractament de la senyal com l'algorisme d'estimació. El filtre pas baix ha estat clau per "salvar" trames de veu de baixa energia (reduint el *Voiced as unvoiced* significativament), mentre que el filtre de mediana ha garantit la continuïtat melòdica eliminant els *spikes* o errors d'octava aïllats. + +* **Importància de l'Ajust de Paràmetres (Grid Search):** L'ús dels scripts d'optimització ha permès trobar un punt d'equilibri crític. L'ajust del llindar de ZCR a 0.10, la potència a -50 dB, l'umbral rmaxnorm a 0.34 i l'umbral r1norm a 0.23 han permès que el sistema sigui prou sensible per detectar veu feble però prou selectiu per no disparar-se amb el soroll de fons. + +* **Compromís Precisió-Robustesa:** S'observa que, a mesura que el sistema es torna més robust contra errors grossos, el MSE (error fi) tendeix a estabilitzar-se al voltant del 2.7%. Això és un compromís acceptable. + +En resum, la versió final del sistema representa un detector de pitch d'alta fiabilitat, que combina el millor dels dominis temporal i freqüencial per oferir una resposta estable i precisa davant de locucions diverses. + + Evaluación *ciega* del estimador ------------------------------- diff --git a/features.txt b/features.txt new file mode 100644 index 0000000..e69de29 diff --git a/img/Docopt.png b/img/Docopt.png new file mode 100644 index 0000000..d4e60e0 Binary files /dev/null and b/img/Docopt.png differ diff --git a/img/grafica_comparativa_pitch.png b/img/grafica_comparativa_pitch.png new file mode 100644 index 0000000..06326c7 Binary files /dev/null and b/img/grafica_comparativa_pitch.png differ diff --git a/img/grafica_completa_sonoritat.png b/img/grafica_completa_sonoritat.png new file mode 100644 index 0000000..c7bbbd3 Binary files /dev/null and b/img/grafica_completa_sonoritat.png differ diff --git a/img/grafica_pitch.png 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+0 +0 +0 +0 +0 diff --git a/plot_pitch.py b/plot_pitch.py new file mode 100644 index 0000000..5cdbaf3 --- /dev/null +++ b/plot_pitch.py @@ -0,0 +1,104 @@ +import matplotlib +matplotlib.use('Agg') +import numpy as np +import matplotlib.pyplot as plt +from scipy.io import wavfile + +# Llegir l'arxiu d'àudio +fs, data = wavfile.read('prueba.wav') + +# Assegurar-nos que és mono +if len(data.shape) > 1: + data = data[:, 0] + +# Si és sencer de 16 bits, convertim a float i normalitzem +if data.dtype == np.int16: + data = data.astype(np.float32) / 32768.0 +else: + data = data.astype(np.float32) +data = data / np.max(np.abs(data)) + +# Buscarem un segment sonor de manera automàtica: el de més energia +duracio = 0.030 # 30 ms +mida_finestra = int(duracio * fs) +energia = np.convolve(data**2, np.ones(mida_finestra), mode='valid') +inici_mostra = np.argmax(energia) +final_mostra = inici_mostra + mida_finestra +segment = data[inici_mostra:final_mostra] + +start_time = inici_mostra / fs + +# Vector de temps pel segment +temps_segment = np.linspace(0, duracio, len(segment), endpoint=False) * 1000 # en ms + +# Càlcul de l'autocorrelació +autocorr = np.correlate(segment, segment, mode='full') +# Ens quedem amb la meitat dreta (lags positius) +autocorr = autocorr[len(autocorr)//2:] +# Normalitzem respecte al màxim (a lag 0) +if np.max(autocorr) != 0: + autocorr = autocorr / np.max(autocorr) + +# Trobem el primer màxim secundari (el pitch) +# Trobem on la derivada canvia de negativa a positiva (mínim) o passa per zero +zero_crossings = np.where(np.diff(np.signbit(autocorr)))[0] +if len(zero_crossings) > 0: + first_zero = zero_crossings[0] +else: + first_zero = 20 # valor per defecte si no creua zero + +# Limitem la cerca a freqüències humanes raonables (ex. 50Hz a 500Hz) +min_lag = int(fs / 500) # correspond a 500Hz +max_lag = int(fs / 50) # correspond a 50Hz + +# Busquem a partir del primer pas per zero o mínim raonable +start_search = max(first_zero, min_lag) +end_search = min(max_lag, len(autocorr)) + +if start_search < end_search: + max_idx = np.argmax(autocorr[start_search:end_search]) + start_search +else: + max_idx = start_search + +pitch_period_samples = max_idx +pitch_period_ms = (pitch_period_samples / fs) * 1000 +pitch_freq = fs / pitch_period_samples if pitch_period_samples > 0 else 0 + +# Creació de la figura +fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8)) + +# Subplot 1: Senyal temporal +ax1.plot(temps_segment, segment, color='b') +ax1.set_title(f'Segment sonor de {duracio*1000:.0f} ms (Inici: {start_time:.3f} s)') +ax1.set_xlabel('Temps (ms)') +ax1.set_ylabel('Amplitud') +ax1.grid(True) + +# Marcar el període de pitch en el senyal temporal +# Dibuixem línies verticals separades pel període de pitch estimat +if pitch_period_ms > 0: + num_periods = int(duracio * 1000 / pitch_period_ms) + for i in range(1, num_periods + 1): + ax1.axvline(x=i * pitch_period_ms, color='r', linestyle='--', alpha=0.7) + # Afegim llegenda manualment per a les línies de pitch + ax1.plot([], [], color='r', linestyle='--', label=f'Període de pitch (T={pitch_period_ms:.2f} ms)') + ax1.legend() + +# Subplot 2: Autocorrelació +lags_ms = (np.arange(len(autocorr)) / fs) * 1000 +ax2.plot(lags_ms, autocorr, color='g') +ax2.set_title('Autocorrelació del segment') +ax2.set_xlabel('Retard (ms)') +ax2.set_ylabel('Autocorrelació normalitzada') +ax2.grid(True) + +# Marcar el primer màxim secundari +if pitch_period_ms > 0: + ax2.plot(lags_ms[max_idx], autocorr[max_idx], 'ro', markersize=8, + label=f'Màxim secundari\n(T = {pitch_period_ms:.2f} ms, F0 = {pitch_freq:.1f} Hz)') + ax2.legend() + +plt.tight_layout() +plt.savefig('grafica_pitch.png', dpi=150) +print(f'Gràfica guardada a grafica_pitch.png') +print(f'Freqüència fonamental estimada: {pitch_freq:.1f} Hz') diff --git a/prueba.f0 b/prueba.f0 new file mode 100644 index 0000000..abfca9f --- /dev/null +++ b/prueba.f0 @@ -0,0 +1,200 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 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b/scripts/grid_search.sh @@ -0,0 +1,54 @@ +#!/bin/bash + +best_score=0 +best_params="" + +echo "Iniciant la cerca de paràmetres (Grid Search)..." +echo "Això pot trigar un parell de minuts. Si us plau, espera." +echo "==========================================================" + +# Llistes de valors a provar al voltant dels teus (a OJO) +Z_VALS="0.05 0.10 0.15" +P_VALS="-50 -52 -54" +R1_VALS="0.25 0.30 0.35" +RM_VALS="0.25 0.30 0.35" + +# Assegurem-nos que l'executable està compilat i al dia +make > /dev/null + +for z in $Z_VALS; do + for p in $P_VALS; do + for r1 in $R1_VALS; do + for rm in $RM_VALS; do + + # Executem el test amagant la sortida general + output=$(./scripts/run_get_pitch.sh -c -z $z --pot=$p -1 $r1 -M $rm 2>&1) + + # Extraiem el número de la línia "===> TOTAL: XX.XX %" + score=$(echo "$output" | grep "TOTAL:" | awk '{print $3}') + + # Si hi ha hagut algun error i no troba l'score, el posem a 0 + if [ -z "$score" ]; then + score=0 + fi + + # Mostrem el progrés per pantalla + echo "Provant: -z $z --pot=$p -1 $r1 -M $rm -> Score: $score%" + + # Comparem per si és el nou millor usant awk (per suportar decimals) + is_better=$(echo "$score $best_score" | awk '{if ($1 > $2) print 1; else print 0}') + if [ "$is_better" -eq 1 ]; then + best_score=$score + best_params="-z $z --pot=$p -1 $r1 -M $rm" + fi + + done + done + done +done + +echo "" +echo "=================================================" +echo "LA MILLOR COMBINACIÓ TROBADA ÉS:" +echo "$best_params amb un encert de $best_score%" +echo "=================================================" diff --git a/scripts/grid_search_fine.sh b/scripts/grid_search_fine.sh new file mode 100755 index 0000000..d973612 --- /dev/null +++ b/scripts/grid_search_fine.sh @@ -0,0 +1,54 @@ +#!/bin/bash + +best_score=0 +best_params="" + +echo "Iniciant la cerca FINA de paràmetres (Fine Grid Search)..." +echo "Això pot trigar una mica. Si us plau, espera." +echo "==========================================================" + +# Llistes de valors molt acotats al voltant dels millors resultats (91.26%) +Z_VALS="0.08 0.10 0.12" +P_VALS="-51 -52 -53" +R1_VALS="0.23 0.25 0.27" +RM_VALS="0.23 0.25 0.27" + +# Assegurem-nos que l'executable està compilat i al dia +make > /dev/null + +for z in $Z_VALS; do + for p in $P_VALS; do + for r1 in $R1_VALS; do + for rm in $RM_VALS; do + + # Executem el test amagant la sortida general + output=$(./scripts/run_get_pitch.sh -c -z $z --pot=$p -1 $r1 -M $rm 2>&1) + + # Extraiem el número de la línia "===> TOTAL: XX.XX %" + score=$(echo "$output" | grep "TOTAL:" | awk '{print $3}') + + # Si hi ha hagut algun error i no troba l'score, el posem a 0 + if [ -z "$score" ]; then + score=0 + fi + + # Mostrem el progrés per pantalla + echo "Provant: -z $z --pot=$p -1 $r1 -M $rm -> Score: $score%" + + # Comparem per si és el nou millor usant awk (per suportar decimals) + is_better=$(echo "$score $best_score" | awk '{if ($1 > $2) print 1; else print 0}') + if [ "$is_better" -eq 1 ]; then + best_score=$score + best_params="-z $z --pot=$p -1 $r1 -M $rm" + fi + + done + done + done +done + +echo "" +echo "=================================================" +echo "LA MILLOR COMBINACIÓ TROBADA ÉS:" +echo "$best_params amb un encert de $best_score%" +echo "=================================================" diff --git a/scripts/plot_features.py b/scripts/plot_features.py new file mode 100644 index 0000000..9caf1f5 --- /dev/null +++ b/scripts/plot_features.py @@ -0,0 +1,100 @@ +import sys +import numpy as np +import matplotlib.pyplot as plt + +if len(sys.argv) < 4: + print("Ús: python plot_features.py ") + sys.exit(1) + +features_file = sys.argv[1] +f0_file = sys.argv[2] +f0ref_file = sys.argv[3] + +# 1. LLEGIM LES DADES +# get_pitch imprimeix a stdout: pot, r1norm, rmaxnorm, zcr +try: + features = np.loadtxt(features_file) + pot = features[:, 0] + r1norm = features[:, 1] + rmaxnorm = features[:, 2] + zcr = features[:, 3] +except Exception as e: + print(f"Error llegint features: {e}") + sys.exit(1) + +# El .f0 té un 0 extra a l'inici i al final afegits per get_pitch.cpp +f0 = np.loadtxt(f0_file) +if len(f0) == len(pot) + 2: + f0 = f0[1:-1] # Retallem els extrems perquè coincideixi amb les features + +f0ref = np.loadtxt(f0ref_file) + +# Ajustem les mides per si hi ha petites diferències de longitud +min_len = min(len(f0), len(pot), len(f0ref)) +f0 = f0[:min_len] +f0ref = f0ref[:min_len] +pot = pot[:min_len] +r1norm = r1norm[:min_len] +rmaxnorm = rmaxnorm[:min_len] +zcr = zcr[:min_len] + +# L'eix X és el temps (cada trama avança 15ms segons l'enunciat) +temps = np.arange(min_len) * 0.015 + +# ========================================== +# GRÀFICA 1: COMPARATIVA DE PITCH (El teu vs Referència/Wavesurfer) +# ========================================== +plt.figure(figsize=(10, 5)) +# Piquem els zeros com a NaN perquè no surtin línies verticals lletges +f0_plot = np.where(f0 == 0, np.nan, f0) +f0ref_plot = np.where(f0ref == 0, np.nan, f0ref) + +plt.plot(temps, f0ref_plot, 's', color='lightgray', markersize=6, label='Referència (Wavesurfer)') +plt.plot(temps, f0_plot, '.', color='blue', markersize=4, label='El teu Pitch') +plt.title('Comparativa Estimador de Pitch') +plt.xlabel('Temps (s)') +plt.ylabel('F0 (Hz)') +plt.legend() +plt.grid(True) +plt.tight_layout() +plt.savefig('grafica_comparativa_pitch.png', dpi=300) +print("-> Gràfica desada com a 'grafica_comparativa_pitch.png'") + +# ========================================== +# GRÀFICA 2: ELS CRITERIS DE SONORITAT (pot, r1, rmax, zcr) +# ========================================== +fig, axs = plt.subplots(4, 1, figsize=(10, 10), sharex=True) + +# Potència +axs[0].plot(temps, pot, 'g-') +axs[0].axhline(y=-52, color='r', linestyle='--', alpha=0.7, label='Llindar (-52 dB)') +axs[0].set_ylabel('Potència (dB)') +axs[0].set_title('Evolució de les característiques (Criteris de Sonoritat)') +axs[0].legend(loc="upper right") +axs[0].grid(True) + +# r1norm +axs[1].plot(temps, r1norm, 'm-') +axs[1].axhline(y=0.25, color='r', linestyle='--', alpha=0.7, label='Llindar (0.25)') +axs[1].set_ylabel('r1norm') +axs[1].legend(loc="upper right") +axs[1].grid(True) + +# rmaxnorm +axs[2].plot(temps, rmaxnorm, 'c-') +axs[2].axhline(y=0.25, color='r', linestyle='--', alpha=0.7, label='Llindar (0.25)') +axs[2].set_ylabel('rmaxnorm') +axs[2].legend(loc="upper right") +axs[2].grid(True) + +# ZCR +axs[3].plot(temps, zcr, 'k-') +axs[3].axhline(y=0.10, color='r', linestyle='--', alpha=0.7, label='Llindar (0.10)') +axs[3].set_ylabel('ZCR') +axs[3].set_xlabel('Temps (s)') +axs[3].legend(loc="upper right") +axs[3].grid(True) + +plt.tight_layout() +plt.savefig('grafica_criteris_sonoritat.png', dpi=300) +print("-> Gràfica desada com a 'grafica_criteris_sonoritat.png'") diff --git a/scripts/run_get_pitch.sh b/scripts/run_get_pitch.sh index dcb1b46..4e2d8f2 100755 --- a/scripts/run_get_pitch.sh +++ b/scripts/run_get_pitch.sh @@ -10,7 +10,7 @@ GETF0="get_pitch" for fwav in pitch_db/train/*.wav; do ff0=${fwav/.wav/.f0} echo "$GETF0 $fwav $ff0 ----" - $GETF0 $fwav $ff0 > /dev/null || { echo -e "\nError in $GETF0 $fwav $ff0" && exit 1; } + $GETF0 "$@" $fwav $ff0 > /dev/null || { echo -e "\nError in $GETF0 $fwav $ff0" && exit 1; } done pitch_evaluate pitch_db/train/*.f0ref diff --git a/src/doxyfile/Doxyfile.in b/src/doxyfile/Doxyfile.in index 452dc77..ddb4394 100644 --- a/src/doxyfile/Doxyfile.in +++ b/src/doxyfile/Doxyfile.in @@ -1701,7 +1701,7 @@ FORMULA_MACROFILE = # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. -USE_MATHJAX = NO +USE_MATHJAX = YES # With MATHJAX_VERSION it is possible to specify the MathJax version to be used. # Note that the different versions of MathJax have different requirements with diff --git a/src/get_pitch/get_pitch.cpp b/src/get_pitch/get_pitch.cpp index c13432d..fb737aa 100644 --- a/src/get_pitch/get_pitch.cpp +++ b/src/get_pitch/get_pitch.cpp @@ -4,6 +4,8 @@ #include #include #include +#include +#include #include "wavfile_mono.h" #include "pitch_analyzer.h" @@ -13,9 +15,13 @@ #define FRAME_LEN 0.030 /* 30 ms. */ #define FRAME_SHIFT 0.015 /* 15 ms. */ +#include + using namespace std; using namespace upc; + + static const char USAGE[] = R"( get_pitch - Pitch Estimator @@ -23,11 +29,15 @@ get_pitch - Pitch Estimator get_pitch [options] get_pitch (-h | --help) get_pitch --version - Options: + -p, --pot FLOAT llindar de potència per la decisió sonor/sord [Default: -50] + -1, --r1norm FLOAT llindar de correlació de 1 per la decisió sonor/sord [Default: 0.23] + -M, --rmaxnorm FLOAT llindar de correlació al max secundari per la decisió sonor/sord [Default: 0.34] + -z, --zcr FLOAT llindar de taxa de zero cross rate [Default: 0.1] + -c, --activar_ceps Activa el càlcul del Cepstrum per trobar el pitch [Default: true] -h, --help Show this screen --version Show the version of the project - + Arguments: input-wav Wave file with the audio signal output-txt Output file: ASCII file with the result of the estimation: @@ -39,6 +49,9 @@ int main(int argc, const char *argv[]) { /// \TODO /// Modify the program syntax and the call to **docopt()** in order to /// add options and arguments to the program. + + /// \DONE + /// Hemos añadido los parámetros de zero crossing, cepstrum, los lindares de correlación r1norm y rmaxnorm y el lindar de potencia. std::map args = docopt::docopt(USAGE, {argv + 1, argv + argc}, // array of arguments, without the program name true, // show help if requested @@ -46,6 +59,11 @@ int main(int argc, const char *argv[]) { std::string input_wav = args[""].asString(); std::string output_txt = args[""].asString(); + float llindar_pot = stof(args["--pot"].asString()); + float llindar_zcr = stof(args["--zcr"].asString()); + float llindar_r1norm = stof(args["--r1norm"].asString()); + float llindar_rmaxnorm = stof(args["--rmaxnorm"].asString()); + bool activar_ceps = args["--activar_ceps"].asBool(); // Read input sound file unsigned int rate; @@ -58,13 +76,34 @@ int main(int argc, const char *argv[]) { int n_len = rate * FRAME_LEN; int n_shift = rate * FRAME_SHIFT; - // Define analyzer - PitchAnalyzer analyzer(n_len, rate, PitchAnalyzer::RECT, 50, 500); + // Define analyzer --> Constructor, passar llindars + PitchAnalyzer analyzer(n_len, rate, PitchAnalyzer::HAMMING, 50, 500, llindar_pot, llindar_r1norm, llindar_rmaxnorm, llindar_zcr, activar_ceps); /// \TODO /// Preprocess the input signal in order to ease pitch estimation. For instance, /// central-clipping or low pass filtering may be used. - + + /// \DONE + /// Hemos aplicado un filtro paso bajo de media 5 muestras sobre la señal completa. + /// El objetivo es atenuar componentes espectrales de alta frecuencia que no corresponden al + /// rango del pitch humano, mejorando así la robustez del estimador y disminuyendo los Gross Errors. + +//Preprocesado: filtro paso-bajo +if (x.size() > 5) { + vector x_filtered = x; + int navg = 5; // Tamaño de la ventana del filtro + + for (size_t i = navg; i < x.size(); ++i) { + float sum = 0.0; + for (int j = 0; j < navg; ++j) { + sum += x[i - j]; + } + x_filtered[i] = sum / navg; + } + x = x_filtered; // Sustituimos la señal original por la filtrada + } + + // Iterate for each frame and save values in f0 vector vector::iterator iX; vector f0; @@ -76,6 +115,30 @@ int main(int argc, const char *argv[]) { /// \TODO /// Postprocess the estimation in order to supress errors. For instance, a median filter /// or time-warping may be used. + + + + /// \DONE + /// Hemos implementado un filtro de mediana de longitud 3 como etapa de postprocesado. + /// Este filtro analiza cada valor junto su anterior y su posterior valor para eliminar errores atípicos. + /// Al seleccionar el valor central de la ventana ordenada, conseguimos suavizar la curva de f0 + /// y reducir el Gross Voiced Error. + + /// Postprocess: Filtro de mediana de longitud 3 + vector f0_filtered = f0; // Copia para no modificar mientras leemos + + for (size_t i = 1; i < f0.size() - 1; ++i) { + // Tenemos que crear un vector con tres valores para poder hacer la mediana --> valor anterior, el actual y el siguiente + vector vectdemediana = {f0[i-1], f0[i], f0[i+1]}; + + // Ordenamos los 3 valores + sort(vectdemediana.begin(), vectdemediana.end()); + + // El valor del medio (índice 1) es la mediana + f0_filtered[i] = vectdemediana[1]; + } + f0 = f0_filtered; // Actualizamos el vector original + // Write f0 contour into the output file ofstream os(output_txt); diff --git a/src/get_pitch/pitch_analyzer.cpp b/src/get_pitch/pitch_analyzer.cpp index effbb48..72920d0 100644 --- a/src/get_pitch/pitch_analyzer.cpp +++ b/src/get_pitch/pitch_analyzer.cpp @@ -1,7 +1,8 @@ /// @file #include -#include +#include +#include #include "pitch_analyzer.h" using namespace std; @@ -11,7 +12,25 @@ namespace upc { void PitchAnalyzer::autocorrelation(const vector &x, vector &r) const { for (unsigned int l = 0; l < r.size(); ++l) { - /// \TODO Compute the autocorrelation r[l] + /// \TODO Compute the autocorrelation r[l] + /** + \DONE Autocorrelación calculada + \f[ + r[l] = \frac{1}{N} \sum_{n=l}^{n=N} x[n] \cdot x[n-l] + \f] + + + 1. Inicialitzem \f$r[l]\f$ a zero + 2. Acumulem el producte de \f$x[n]\f$ per \f$x[n-l]\f$ per a \f$n\f$ des de \f$l\f$ fins a \f$N-1\f$ + 3. Dividim \f$r[l]\f$ entre \f$N\f$. + */ + + r[l]=0.0F; + for (unsigned int n=l; n frameLen/2) npitch_max = frameLen/2; } - bool PitchAnalyzer::unvoiced(float pot, float r1norm, float rmaxnorm) const { + bool PitchAnalyzer::unvoiced(float pot, float r1norm, float rmaxnorm, float zcr) const { /// \TODO Implement a rule to decide whether the sound is voiced or not. /// * You can use the standard features (pot, r1norm, rmaxnorm), /// or compute and use other ones. - return true; + + /** * \DONE + * Implementación de la decisión sonoro/sordo (voiced/unvoiced): + * La señal se considera sonora (false) si la correlación de primer orden (r1norm) + * o la correlación en el máximo secundario (rmaxnorm) superan el umbral de 0.6. + * En caso contrario, se considera sorda (true). + * Además la potencia i la zcr también se tiene en cuenta: + * Si la potencia es inferior al umbral es sordo. + * Si la tasa de cruce por cero es alta es sordo + */ + + /// Si la potencia es inferior al umbral es sordo + if (pot < llindar_pot) { + return true; + } + + /// Si la periodicidad es baja, es sordo (unvoiced) + /// Usamos los nombres de variables definidas en el docopt + if (r1norm < llindar_r1norm || rmaxnorm < llindar_rmaxnorm) { + return true; + } + /// Si la tasa de cruce por cero es alta es sordo + if (zcr > llindar_zcr) { + return true; + } + + return false; + } float PitchAnalyzer::compute_pitch(vector & x) const { if (x.size() != frameLen) return -1.0F; - //Window input frame + /// Window input frame for (unsigned int i=0; i r(npitch_max); - - //Compute correlation - autocorrelation(x, r); + vector r(npitch_max), c(npitch_max); + + + float pot = 0.0f; + for (unsigned int i=0; i::const_iterator iR, iRMax, iter; + /// \TODO Find the lag of the maximum value of the autocorrelation away from the origin. + /// Choices to set the minimum value of the lag are: + /// The first negative value of the autocorrelation. + /// The lag corresponding to the maximum value of the pitch. + /// . + /// In either case, the lag should not exceed that of the minimum value of the pitch. + + /** *\DONE + * Hemos localizado el lag del valor máximo de la autocorrelación fuera del origen. + * Se ha optado por iniciar la búsqueda en npitch_min, que corresponde al periodo + * de la frecuencia de pitch máxima permitida (opción 2 del enunciado). + */ + + bool usar_cepstrum = activar_ceps; + iter = usar_cepstrum ? c.begin() : r.begin(); + for(iR= iRMax = iter + npitch_min ; iR < iter + npitch_max ; iR++){ + if (*iR > *iRMax){ + iRMax =iR; + } + } + + unsigned int lag = iRMax - iter; + + // Si hem usat el cepstrum, el pic d'autocorrelació pot estar lleugerament desplaçat. + // Busquem el màxim local de l'autocorrelació al voltant del lag trobat. + float r_max_val = r[lag]; + if (activar_ceps) { + int search_range = 3; // Marge de cerca + for (int k = -search_range; k <= search_range; ++k) { + int current_lag = lag + k; + if (current_lag >= 0 && current_lag < (int)r.size()) { + if (r[current_lag] > r_max_val) { + r_max_val = r[current_lag]; + } + } + } + } - vector::const_iterator iR = r.begin(), iRMax = iR; + // pot already calculated + + // Calcular el numero de cruces por cero + float zcr = 0.0f; + for (unsigned int i = 1; i < x.size(); ++i) { + if (x[i] * x[i-1] < 0.0f) { + zcr += 1.0f; + } + } + zcr = zcr / x.size(); - /// \TODO - /// Find the lag of the maximum value of the autocorrelation away from the origin.
- /// Choices to set the minimum value of the lag are: - /// - The first negative value of the autocorrelation. - /// - The lag corresponding to the maximum value of the pitch. - /// . - /// In either case, the lag should not exceed that of the minimum value of the pitch. + // Unvoiced decision using power, r[1]/r[0], r_max_val/r[0], and zcr + if (unvoiced(pot, r[1]/r[0], r_max_val/r[0], zcr)) { + return 0; + } - unsigned int lag = iRMax - r.begin(); + return (float) samplingFreq/(float) lag; + } - float pot = 10 * log10(r[0]); + void PitchAnalyzer::cepstrum(const vector &x, vector &c) const { + // 1. Zero Padding (cal fer-la amb mida potència de 2, tipus 2^ceil(log2(N))) + // 2. |X| = sqrt(real^2 + imag^2) + // 3. log(|X| + epsilon) + // 4. IFFT del log-espectre + // 5. c[n] = part real de la IFFT + + // Assegurar que N cobreix fins a lag=320 sense fer aliasing (N > 2*npitch_max) + unsigned int N = 1024; + while (N < x.size() * 2) N <<= 1; + ffft::FFTReal fft(N); + // 1. Pre-èmfasi i Zero-padding + vector buf(N, 0.0f); + buf[0] = x[0]; + for(unsigned int i = 1; i < x.size(); ++i) { + buf[i] = x[i] - 0.97f * x[i-1]; // Filtre de pre-èmfasi + } + + // 2. FFT + vector spec(N); + fft.do_fft(spec.data(), buf.data()); + + // 3. Log-magnitud (format packed) + vector logmag(N/2 + 1); + for (unsigned int k = 0; k <= N/2; ++k) { + float re = spec[k]; + float im = (k == 0 || k == N/2) ? 0.0f : spec[N/2 + k]; + logmag[k] = log(sqrt(re*re + im*im) + 1e-10f); + } + + // 4. Omplir part real, imag = 0 + fill(spec.begin(), spec.end(), 0.0f); + copy(logmag.begin(), logmag.end(), spec.begin()); + + + // 5. IFFT --> cepstrum + fft.do_ifft(spec.data(), buf.data()); + fft.rescale(buf.data()); + + // 6. Copiar a c + for (unsigned int i = 0; i < c.size(); ++i) + c[i] = buf[i]; +} - //You can print these (and other) features, look at them using wavesurfer - //Based on that, implement a rule for unvoiced - //change to #if 1 and compile -#if 0 - if (r[0] > 0.0F) - cout << pot << '\t' << r[1]/r[0] << '\t' << r[lag]/r[0] << endl; -#endif - if (unvoiced(pot, r[1]/r[0], r[lag]/r[0])) - return 0; - else - return (float) samplingFreq/(float) lag; - } + + } + diff --git a/src/get_pitch/pitch_analyzer.h b/src/get_pitch/pitch_analyzer.h index 684c49f..9876132 100644 --- a/src/get_pitch/pitch_analyzer.h +++ b/src/get_pitch/pitch_analyzer.h @@ -30,11 +30,20 @@ namespace upc { samplingFreq, ///< sampling rate (in samples per second). Has to be set in the constructor call npitch_min, ///< minimum value of pitch period, in samples npitch_max; ///< maximum value of pitch period, in samples - + float llindar_pot; ///< llindar de potencia + float llindar_r1norm;///< llindar de correlacio de 1 + float llindar_rmaxnorm;///< llindar de correlació al max fora de l'origen + float llindar_zcr;///< llindar de taxa de creuament per zero + bool activar_ceps;///< activar cepstrum (true) o autocorrelacio (false) + /// /// Computes correlation from lag=0 to r.size() /// void autocorrelation(const std::vector &x, std::vector &r) const; + /// + /// Computes cepstrum from lag=0 to c.size() + /// + void cepstrum(const std::vector &x, std::vector &c) const; /// /// Returns the pitch (in Hz) of input frame x @@ -44,7 +53,7 @@ namespace upc { /// /// Returns true is the frame is unvoiced /// - bool unvoiced(float pot, float r1norm, float rmaxnorm) const; + bool unvoiced(float pot, float r1norm, float rmaxnorm, float zcr) const; public: @@ -52,13 +61,24 @@ namespace upc { unsigned int sFreq, ///< Sampling rate in Hertzs Window w=PitchAnalyzer::HAMMING, ///< Window type float min_F0 = MIN_F0, ///< Pitch range should be restricted to be above this value - float max_F0 = MAX_F0 ///< Pitch range should be restricted to be below this value + float max_F0 = MAX_F0, ///< Pitch range should be restricted to be below this value + float llindar_pot = -52, ///< llindar de potencia per la decisió sonor/sord + float llindar_r1norm = 0.23, ///llindar_pot = llindar_pot; + this->llindar_r1norm = llindar_r1norm; + this->llindar_rmaxnorm = llindar_rmaxnorm; + this->llindar_zcr = llindar_zcr; + this->activar_ceps = activar_ceps; + } /// diff --git a/test_nuevo.f0 b/test_nuevo.f0 new file mode 100644 index 0000000..134d6b1 --- /dev/null +++ b/test_nuevo.f0 @@ -0,0 +1,134 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +143.885 +152.672 +158.73 +163.934 +168.067 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +144.928 +142.857 +138.889 +134.228 +129.87 +125 +120.482 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +116.959 +118.343 +116.959 +116.279 +0 +0 +0 +118.343 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0