Gammatone Cepstral Coefficients: Biologically Inspired Features for Non-Speech Audio Classification

Valero, Xavier; Álías, Francesc

doi:10.1109/tmm.2012.2199972

Cited by 222 publications

(134 citation statements)

References 16 publications

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“…Two types of analysis have been conducted after the parametrization of the computed spectral envelopes of each database using Gammatone Cepstral Coefficients (GTCC) [11]:…”

Section: Discussionmentioning

confidence: 99%

Detecting Anomalous Noise Events on Low-Capacity Acoustic Sensor in Dynamic Road Traffic Noise Mapping

Alsina-Pagès

Socoró

Álías

2018

The 4th International Electronic Conference on Sensors and Applications

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Abstract:One of the main aspects affecting the life of people living in urban and suburban areas is their continued exposure to high road traffic noise (RTN) levels, traditionally measured by specialists working on the field. Nowadays, the deployment of Wireless Acoustic Sensor Networks (WASN) has allowed to automate noise mapping in Smart Cities. In order to obtain a reliable picture of the RTN levels affecting citizens, those anomalous noise events (ANE) unrelated to road traffic should be removed from the noise map computation. For this purpose, an Anomalous Noise Event Detector (ANED) designed to differentiate in real-time between RTN and ANE should be developed to run on the low-cost acoustic sensors of the WASN. In this work, the viability of implementing the ANED algorithm to run on low-capacity (LowCap) µ controller-based acoustic sensors developed within the DYNAMAP project is presented, after being designed and implemented for the high-capacity sensors. The algorithm is based on the comparison between RTN and ANE spectral differences using real-life acoustic data from both suburban and urban scenarios. The results show significant spectral differences between RTN and ANE classes in both environments, after being parametrized using Gammatone Cepstral Coefficients. However, further research should be conducted to determine the most discriminant subbands, which should be taken into account for the implementation of the ANED LowCap version.

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“…Two types of analysis have been conducted after the parametrization of the computed spectral envelopes of each database using Gammatone Cepstral Coefficients (GTCC) [11]:…”

Section: Discussionmentioning

confidence: 99%

Detecting Anomalous Noise Events on Low-Capacity Acoustic Sensor in Dynamic Road Traffic Noise Mapping

Alsina-Pagès

Socoró

Álías

2018

The 4th International Electronic Conference on Sensors and Applications

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“…Feature implementations are available online 3 , which contribute to reproducibility of results. Other features were considered, such as CQCC [TDE16], PNCC [KS16], and GFCC [VA12], but in our previous tests they did not improve the performance of the jointly fused PAD system.…”

Section: Featuresmentioning

confidence: 97%

On the Generalization of Fused Systems in Voice Presentation Attack Detection

Gonçalves

Violato

Korshunov

et al. 2017

2017 International Conference of the Biometrics Special Interest Group (BIOSIG)

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This paper describes presentation attack detection systems developed for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017). The submitted systems, using calibration and score fusion techniques, combine different sub-systems (up to 18), which are based on eight state of the art features and rely on Gaussian mixture models and feedforward neural network classifiers. The systems achieved the top five performances in the competition. We present the proposed systems and analyze the calibration and fusion strategies employed. To assess the systems' generalization capacity, we evaluated it on an unrelated larger database recorded in Portuguese language, which is different from the English language used in the competition. These extended evaluation results show that the fusion-based system, although successful in the scope of the evaluation, lacks the ability to accurately discriminate genuine data from attacks in unknown conditions, which raises the question on how to assess the generalization ability of attack detection systems in practical application scenarios.

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“…The field of Computational Auditory Scene Analysis (CASA) is a fast moving area, with international challenges such as DCASE 1 to accelerate the progress on complex audio recognition problems. Previously, methods based on feature engineering from speech and music domain such as MFCCs, Linear Predictive Coefficients and Gammatone Cepstral Coefficients [1] have been used to extract features for ASC.…”

Section: Introductionmentioning

confidence: 99%

“…Previously, methods based on feature engineering from speech and music domain such as MFCCs, Linear Predictive Coefficients and Gammatone Cepstral Coefficients [1] have been used to extract features for ASC.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach with multi-channel i-vectors and convolutional neural networks for acoustic scene classification

Eghbal-zadeh

Lehner

Dorfer

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st rank among 49 submissions, substantially improving the previous state of the art.

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Gammatone Cepstral Coefficients: Biologically Inspired Features for Non-Speech Audio Classification

Cited by 222 publications

References 16 publications

Detecting Anomalous Noise Events on Low-Capacity Acoustic Sensor in Dynamic Road Traffic Noise Mapping

Detecting Anomalous Noise Events on Low-Capacity Acoustic Sensor in Dynamic Road Traffic Noise Mapping

On the Generalization of Fused Systems in Voice Presentation Attack Detection

A hybrid approach with multi-channel i-vectors and convolutional neural networks for acoustic scene classification

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