NMF-based temporal feature integration for acoustic event classification

Proceedings of: 14th Annual Conference of the International Speech Communication Association. Lyon, France, 25-29 August 2013.In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC) based on the combination of the temporal feature integration technique called Filter Bank Coefficients (FC) and Non-Negative Matrix Factorization (NMF). FC aims to capture the dynamic structure in the short-term features by means of the summarization of the periodogram of each short-term feature dimension in several frequency bands using a predefined filter bank. As the commonly used filter bank has been devised for other tasks (such as music genre classification), it can be suboptimal for AEC. In order to overcome this drawback, we propose an unsupervised method based on NMF for learning the filters which collect the most relevant temporal information in the short-time features for AEC. The experiments show that the features obtained with this method achieve significant improvements in the classification performance of a Support Vector Machine (SVM) based AEC system in comparison with the baseline FC features.This work has been partially supported by the Spanish Government grants TSI-020110-2009-103, IPT-120000-2010-24 and TEC2011-26807Publicad

Contribuciones a la aplicación de la factorización de matrices no negativas a las tecnologías del habla

El funcionamiento de los sistemas de procesamiento y clasificación de audio (incluida la voz) en escenarios reales, depende, en gran medida, de una adecuada representación de la señal de audio, tanto en condiciones limpias como ruidosas. Por este motivo, en esta Tesis abordamos la problemática del diseño de nuevos esquemas de preprocesamiento y extracción de características acústicas con aplicación a dos tareas distintas: reconocimiento automático del habla y clasificación de eventos acústicos. El nexo de unión de los métodos propuestos es la utilización de la técnica denominada factorización de matrices no negativas (NMF, Non-Negative Matrix Factorization) que ha demostrado ser una herramienta poderosa para el análisis de la señal de audio. En primer lugar, en este trabajo de tesis se propone un método de eliminación de ruido en señales de voz basado en NMF, que, a diferencia de otras aproximaciones previas, no asume un conocimiento a priori acerca de la naturaleza del ruido. La técnica es evaluada tanto para mejora de voz como para reconocimiento automático de habla mostrando un mejor funcionamiento que la técnica convencional de sustracción espectral. En segundo lugar, se proponen tres parametrizaciones novedosas para la tarea de clasificación de eventos acústicos. La primera de ellas es una extensión de los parámetros convencionales mel-cepstrales y consiste en el filtrado paso alto de la señal de audio. El segundo esquema consiste en una mejora de la técnica de integración temporal de características llamada coeficientes de banco de filtros (FC, Filter bank Coe_cients) en el que NMF se utiliza como método no supervisado para el aprendizaje del banco de filtros FC óptimo. Finalmente, en el último nuevo parametrizador se propone la inclusión de características cepstrales derivadas de los coeficientes de activación o ganancia de NMF, motivada por la robustez al ruido que NMF ofrece. Los experimentos realizados muestran que, en términos generales, estos tres esquemas mejoran el funcionamiento del sistema de clasificación de eventos acústicos con respecto al de referencia tanto en condiciones limpias como ruidosas.In real scenarios, the performance of audio processing and classiffication systems depends largely on an adequate representation of the signal in both clean and noisy conditions. Therefore, in this Thesis we face the problem of designing new methods to preprocess audio signals and extract acoustic features with the intention of being applied to two different tasks: Automatic Speech Recognition (ASR) and Acoustic Event Classification (AEC). The proposed methods are based on the well-known Non-Negative Matrix Factorization (NMF) technique, which has proven to be a powerful tool for analyzing audio signals. Firstly, a method for speech denoising is proposed, that unlike other previous approaches it does not assume a prior knowledge about the nature of the kind of noise. The method is evaluated for both, speech enhancement and ASR, showing better performance than one of the state of art techniques known as Spectral Subtraction (SS). Secondly, we propose three new parameterization schemes for AEC. The first one is an extension of the conventional Mel Frequency Cepstral Coefficients (MFCC) and can be seen as a high-pass filtering of the audio signal. The second scheme is an improvement of the temporal feature integration technique named Filterbank Coefficients (FC), in which the NMF technique is used in an unsupervised manner, allowing to discover an optimal FC Filterbank. Finally, the last new parameterization scheme proposes the use of cepstral features derived from the NMF activation coefficients; this is mainly motivated by the robustness shown by NMF in noisy conditions. Experiments have shown that, in general terms, these three feature extraction modules improve the performance of the acoustic event classification systems with respect to the baseline based on MFCC, for both, clean and noisy conditions with different noises at different signal-to-noise ratio (SNR) levels.Programa de Doctorado en Multimedia y ComunicacionesPresidente: Javier Macías Guarasa.- Secretario: Carmen Peláez Moreno.- Vocal: Rubén San Segundo Hernánde

Feature extraction based on the high-pass filtering of audio signals for Acoustic Event Classification

In this paper, we propose a new front-end for Acoustic Event Classification tasks ( AEC). First, we study the spectral characteristics of different acoustic events in comparison with the structure of speech spectra. Second, from the findings of this study, we propose a new parameterization for AEC, which is an extension of the conventional Mel-Frequency Cepstral Coefficients ( MFCC) and is based on the high pass filtering of the acoustic event signal. The proposed front-end have been tested in clean and noisy conditions and compared to the conventional MFCC in an AEC task. Results support the fact that the high pass filtering of the audio signal is, in general terms, beneficial for the system, showing that the removal of frequencies below 100-275 Hz in the feature extraction process in clean conditions and below 400-500 Hz in noisy conditions, improves significantly the performance of the system with respect to the baseline.This work has been partially supported by the Spanish Government grants IPT-120000-2010-24 and TEC2011-26807. Financial support from the Fundación Carolina and Universidad Católica San Pablo, Arequipa.Publicad

NMF-Based Spectral Analysis for Acoustic Event Classification Tasks

Proceedings of: 6th International Conference The Non-Linear Speech Processing (NOLISP 2013). Mons, Belgium, June 19-21, 2013.In this paper, we propose a new front-end for Acoustic Event Classification tasks (AEC). First, we study the spectral contents of different acoustic events by applying Non-Negative Matrix Factorization (NMF) on their spectral magnitude and compare them with the structure of speech spectra. Second, from the findings of this study, we propose a new parameterization for AEC, which is an extension of the conventional Mel Frequency Cepstrum Coefficients (MFCC) and is based on the high pass filtering of acoustic event spectra. Also, the influence of different frequency scales on the classification rate of the whole system is studied. The evaluation of the proposed features for AEC shows that relative error reductions about 12% at segment level and about 11% at target event level with respect to the conventional MFCC are achieved.This work has been partially supported by the Spanish Government grants TSI-020110-2009-103, IPT-120000-2010-24 and TEC2011-26807. Financial support from the Fundaci´on Carolina and Universidad Católica San Pablo, Arequipa.Publicad

Acoustic Event Classification using spectral band selection and Non-Negative Matrix Factorization-based features

Feature extraction methods for sound events have been traditionally based on parametric representations specifically developed for speech signals, such as the well-known Mel Frequency Cepstrum Coefficients (MFCC). However, the discrimination capabilities of these features for Acoustic Event Classification (AEC) tasks could be enhanced by taking into account the spectro-temporal structure of acoustic event signals. In this paper, a new front-end for AEC which incorporates this specific information is proposed. It consists of two different stages: short-time feature extraction and temporal feature integration. The first module aims at providing a better spectral representation of the different acoustic events on a frame-by-frame basis, by means of the automatic selection of the optimal set of frequency bands from which cepstral-like features are extracted. The second stage is designed for capturing the most relevant temporal information in the short-time features, through the application of Non-Negative Matrix Factorization (NMF) on their periodograms computed over long audio segments. The whole front-end has been evaluated in clean and noisy conditions. Experiments show that the removal of certain frequency bands (which are mainly located in the medium region of the spectrum for clean conditions and in low frequencies for noisy environments) in the short-time feature computation process in conjunction with the NMF technique for temporal feature integration improves significantly the performance of a Support Vector Machine (SVM) based AEC system with respect to the use of conventional MFCCs. (C) 2015 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish Government Grant TEC2014-53390-P. Financial support from the Fundación Carolina and Universidad Católica San Pablo, Arequipa (Jimmy Ludeña-Choez) is thankfully acknowledged

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstrallike features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.This work was supported by the Spanish Government grant TEC2014-53390-P

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. © 2017 Ludeña-Choez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Trabajo de investigació

Spectral Basis Vectors (SBVs) for the twelve bird species sounds.

<p>To improve the readability of the figures, different colors have been used to represent the adjacent spectral basis vectors.</p