Speech enhancement using segmental nonnegative matrix factorization

Fan, Hao-teng; Hung, Jeih-weih; Lu, Xugang; Wang, Syu-Siang

doi:10.1109/icassp.2014.6854450

Cited by 39 publications

(16 citation statements)

References 25 publications

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“…Supervised and unsupervised nonnegative matrix factorization (NMF) methods were investigated in [12,13] for speech enhancement. The basic idea is to decompose the noisy speech data into bases and weights matrices for the speech and noise, respectively.…”

Section: Introductionmentioning

confidence: 99%

SNR-Based Progressive Learning of Deep Neural Network for Speech Enhancement

Gao¹,

Du²,

Dai³

et al. 2016

Interspeech 2016

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In this paper, we propose a novel progressive learning (PL) framework for deep neural network (DNN) based speech enhancement. It aims at decomposing the complicated regression problem of mapping noisy to clean speech into a series of subproblems for enhancing system performances and reducing model complexities. As an illustration, we design a signal-tonoise ratio (SNR) based PL architecture by guiding each hidden layer of the DNN to learn an intermediate target with gradual S-NR gains explicitly. Furthermore, post-processing, with the rich set of information from the multiple learning targets, can further be conducted. Experimental results demonstrate that SNRbased progressive learning can effectively improve perceptual evaluation of speech quality and short-time objective intelligibility in low SNR environments, and reduce the model parameters by 50% when compared with the DNN baseline system. Moreover, when combined with post-processing, the proposed approach can be further improved.

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Section: Introductionmentioning

confidence: 99%

SNR-Based Progressive Learning of Deep Neural Network for Speech Enhancement

Gao¹,

Du²,

Dai³

et al. 2016

Interspeech 2016

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show abstract

“…At the same time, smooth sound can be generating from Artificial Neural Network(ANN) based speech synthesis with a small amount of speech data. Further, it has flexibility in controlling speaker individuality [13]. In ANN based speech synthesis, the system is developed by creating a library of phonemes, a library of phoneme audio files and a dictionary of words with their phoneme representation.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Error Free Punjabi Text to Speech Generation System based on Phonemes

Kaur

Singh

2018

IJERMT

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“…Both ICA and NMF‐based SS can be performed in a supervised learning manner (Jang and Lee ; Fan et al. ; Lin et al. ).…”

Section: Monoaural Audio Source Separationmentioning

confidence: 99%

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Lin

Tsao

2019

Remote Sens Ecol Conserv

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A comprehensive assessment of ecosystem dynamics requires the monitoring of biological, physical and social changes. Changes that cannot be observed visually may be trackable acoustically through soundscape analysis. Soundscapes vary greatly depending on geophysical events, biodiversity and human activities. However, retrieving source-specific information from geophony, biophony and anthropophony remains a challenging task, due to interference by simultaneous sound sources. Audio source separation is a technique that aims to recover individual sound sources when only mixtures are accessible. Here, we review techniques of monoaural audio source separation with the fundamental theories and assumptions behind them. Depending on the availability of prior information about the source signals, the task can be approached as a blind source separation or a model-based source separation. Most blind source separation techniques depend on assumptions about the behaviour of the source signals, and their performance may deteriorate when the assumptions fail. Model-based techniques generally do not require specific assumptions, and the models are directly learned from labelled data. With the recent advances of deep learning, the model-based techniques can yield state-of-the-art separation performance, accordingly facilitate content-based audio information retrieval. Source separation techniques have been adopted in several ecoacoustic applications to evaluate the contributions from biodiversity and anthropogenic disturbance to soundscape dynamics. They can also be employed as nonlinear filters to improve the recognition of bioacoustic signals. To effectively retrieve ecological information from soundscapes, source separation is a crucial tool. We believe that the future integrations of ecological hypotheses and deep learning can realize a high-performance source separation for ecoacoustics, and accordingly improve soundscape-based ecosystem monitoring. Therefore, we outline a roadmap for applying source separation to assist in soundscape information retrieval and hope to promote cross-disciplinary collaboration. (biophony), their habitats (geophony) and human development (anthropophony) (Dumyahn and Pijanowski 2011; Pijanowski et al. 2011; Sueur and Farina 2015; Krause and Farina 2016). By listening to biotic sounds, the composition of soniferous animals can be characterized and applied to assess wildlife biodiversity (Andr e et al. 2011; Saito et al. 2015; Lin et al. 2017a; Ross et al. 2018). On the other hand, abiotic sounds can be used to evaluate the ecosystem dynamics associated with

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Speech enhancement using segmental nonnegative matrix factorization

Cited by 39 publications

References 25 publications

SNR-Based Progressive Learning of Deep Neural Network for Speech Enhancement

SNR-Based Progressive Learning of Deep Neural Network for Speech Enhancement

Error Free Punjabi Text to Speech Generation System based on Phonemes

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

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