A High SIR Low-overhead Implementation of Single-channel Speech Source Separation

Lawrence Nwaogo*, Jerker Björkqvist

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

In the field of speech signal processing, speech source mixture separation is a known challenge. It is addressed by finding the closest estimate of the original speech source from the speech mixture. Source separation solutions can be based on multiple channels or single channel model. In multiple channels, multiple speakers and microphones are assumed while in single channel multiple speakers and a single microphone are assumed. One of the most widely used algorithms in the single-channel model is the Ideal Ratio Mask (IRM). Although IRM is efficient, it has a major drawback; the high memory footprint as it stores all frequency components of the Short-time Fourier transform (STFT). This makes it less suitable for embedded applications. We propose a solution based on the optimization of Mel-frequency Cepstrum Coefficient (MFCC) and Non-centroid K-nearest neighbor (Nk-nn) algorithms that minimizes memory utilization and achieves high Signal-to-Interference Ratio (SIR). Our experimental results show that the proposed solution improves SIR while minimizing memory requirements compared to the reference IRM.
AlkuperäiskieliEnglanti
Otsikko2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)
KustantajaIEEE
Sivut440-444
ISBN (elektroninen)9781665406338
ISBN (painettu)978-1-6654-0634-5
DOI - pysyväislinkit
TilaJulkaistu - 22 heinäk. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaSensor Array and Multichannel Signal Processing Workshop -
Kesto: 20 kesäk. 2022 → …

Konferenssi

KonferenssiSensor Array and Multichannel Signal Processing Workshop
Ajanjakso20/06/22 → …

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