Speaker-Independent Speech Separation with Deep Attractor Network

Speaker-Independent Speech Separation with Deep Attractor Network

Registration ID: IJNRD_209228

Published ID: IJNRD2312148

DOI: Click Here to Get

Prof. Pooja Rasane , Harshada Bhujbal , Omkar Dhore , Maithili Jagdale , Sandhyarani Sonkamble

Speech Separation, DNN, Feature Extraction, Deep Learning, Speaker- Independent

In recent years, deep learning-based methods have significantly improved the performance of speech separation. However, a challenging problem remains: how to separate the speech of unknown speakers in a mixture, a problem known as speaker-independent speech separation. This research presents an innovative deep learning framework for speech separation that addresses the issue of unknown speakers in the mixture. We propose a neural network that projects the time-frequency representation of the mixture signal into a high-dimensional feature space. Within this space, reference points (attractors) are created to represent each speaker, defined as the centroid of the speaker in the embedding space. The time-frequency embeddings of each speaker are then encouraged to cluster around their corresponding attractor points, which are used to determine the time-frequency assignment of each speaker. This approach enhances the robustness of speech separation for various applications, including speech recognition, speaker diarization, and more.

Published Paper   E-Certificate

"Speaker-Independent Speech Separation with Deep Attractor Network", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 12, page no.b277-b279, December-2023, Available :https://ijnrd.org/papers/IJNRD2312148.pdf

Volume 8 Issue 12, December-2023

Pages : b277-b279

Paper Reg. ID: IJNRD_209228

Published Paper Id: IJNRD2312148

Downloads: 000121254

Research Area: Engineering

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312148.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312148

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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