Speech Enhancement in Multiple-Noise Conditions Using Deep Neural Networks

By Anurag Kumar, Dinei A. F. Florêncio. 2016

126 citations recorded by the source corpus.

In this paper we consider the problem of speech enhancement in real-world like conditions where multiple noises can simultaneously corrupt speech. Most of the current literature on speech enhancement focus primarily on presence of single noise in corrupted speech which is far from real-world environments. Specifically, we deal with improving speech quality in office environment where multiple stationary as well as non-stationary noises can be simultaneously present in speech. We propose several strategies based on Deep Neural Networks (DNN) for speech enhancement in these scenarios. We also investigate a DNN training strategy based on psychoacoustic models from speech coding for enhancement of noisy speech

Subfields: Cognitive Neuroscience, Computational Mechanics, Speech & Audio, Signal Processing

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Citations in the corpus

A Wavenet for Speech Denoising (2017, background): “Similarly, most techniques for speech denoising use magnitude spectrograms as front-end [13, 17, 21, 34, 36].”
SEGAN: Speech Enhancement Generative Adversarial Network (2017, background): “In [15] and [16], noise features are estimated and included in the input features of deep neural networks.”
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding (2020, background): “perceptual attractors [11], energy-based weighting [12], perceptual weighting filters from speech coding [13], and global masking thresholds [14] [15], while they have not targeted audio coding and model compression.”
Improving GANs for Speech Enhancement (2020, background): “Significant progress on this research topic has been made with the involvement of deep learning paradigms such as deep neural networks (DNNs) [4], [5], convolutional neural networks (CNNs) [6], [7], and recurrent neural networks (RNNs) [3], [8].”
Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis (2022, unknown): “…methods, which address the problem of separating a mixture of speakers from a single audio track [23, 36, 60, 67], as well as monaural speech enhancement methods, which tackle the problem of removing non-speech background sounds [8, 30, 42, 55, 56, 63] and rever-beration [8, 56] from noisy speech.”
VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency (2021, background): “Speech separation is a special case of sound source separation where the goal is to separate the speech signal of a target speaker from background interference, including nonspeech noise [75, 42] and/or interfering speech from other speakers [79, 48].”