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International Journal of Speech Technology

Erschienen in:

09.05.2020

A study on unsupervised monaural reverberant speech separation

verfasst von: R. Hemavathi, R. Kumaraswamy

Erschienen in: International Journal of Speech Technology | Ausgabe 2/2020

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Abstract

Separating individual source signals is a challenging task in musical and multitalker source separation. This work studies unsupervised monaural (co-channel) speech separation (UCSS) in reverberant environment. UCSS is the problem of separating the individual speakers from multispeaker speech without using any training data and with minimum information regarding mixing condition and sources. In this paper, state-of-art UCSS algorithms based on auditory and statistical approaches are evaluated for reverberant speech mixtures and results are discussed. This work also proposes to use multiresolution cochleagram and Constant Q Transform (CQT) spectrogram feature with two-dimensional Non-negative matrix factorization. Results show that proposed algorithm with CQT spectrogram feature gave an improvement of 1.986 and 1.262 in terms of speech intelligibility and 0.296 db and 0.561 db in terms of signal to interference ratio compared to state-of-art statistical and auditory approach respectively at T60 of 0.610s.

Vorheriger Artikel Speaker recognition based on short utterance compensation method of generative adversarial networks

Nächster Artikel Order and phase ambiguities correction in the ICA based separation of speech signals

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Titel: A study on unsupervised monaural reverberant speech separation
verfasst von: R. Hemavathi
R. Kumaraswamy
Publikationsdatum: 09.05.2020
Verlag: Springer US
Erschienen in: International Journal of Speech Technology / Ausgabe 2/2020
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI: https://doi.org/10.1007/s10772-020-09706-x

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