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On the Use of Audio Fingerprinting Features for Speech Enhancement with Generative Adversarial Network

2020-07-27 00:44:16
Farnood Faraji, Yazid Attabi, Benoit Champagne, Wei-Ping Zhu
arXiv_SD
           
Abstract

The advent of learning-based methods in speech enhancement has revived the need for robust and reliable training features that can compactly represent speech signals while preserving their vital information. Time-frequency domain features, such as the Short-Term Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC), are preferred in many approaches. While the MFCC provide for a compact representation, they ignore the dynamics and distribution of energy in each mel-scale subband. In this work, a speech enhancement system based on Generative Adversarial Network (GAN) is implemented and tested with a combination of Audio FingerPrinting (AFP) features obtained from the MFCC and the Normalized Spectral Subband Centroids (NSSC). The NSSC capture the locations of speech formants and complement the MFCC in a crucial way. In experiments with diverse speakers and noise types, GAN-based speech enhancement with the proposed AFP feature combination achieves the best objective performance while reducing memory requirements and training time.

Abstract (translated)

URL

https://arxiv.org/abs/2007.13258

PDF

https://arxiv.org/pdf/2007.13258.pdf

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