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A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

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Speaker-dependent multipitch tracking using deep neural networks.

Yuzhou Liu¹, DeLiang Wang¹

¹Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.

The Journal of the Acoustical Society of America

|March 4, 2017

Summary

This summary is machine-generated.

This study introduces deep neural networks (DNNs) for accurate multipitch tracking in two-speaker audio. The novel methods improve speaker assignment and pitch estimation, outperforming existing techniques.

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Area of Science:

Signal Processing
Machine Learning
Speech Recognition

Background:

Multipitch tracking is crucial for analyzing simultaneous speech.
Accurate pitch estimation and speaker assignment remain significant challenges.

Purpose of the Study:

To develop advanced deep neural network (DNN) models for robust multipitch tracking.
To improve simultaneous speaker identification and pitch estimation accuracy.

Main Methods:

Utilized speaker-dependent and speaker-pair-dependent DNNs to model probabilistic pitch states.
Incorporated extensions like gender-pair-dependent DNNs and speaker adaptation.
Employed a factorial hidden Markov model (FHMM) with a junction tree algorithm for pitch track generation.

Main Results:

Proposed DNN-based methods significantly outperformed existing speaker-independent and speaker-dependent trackers.
Multi-ratio training ensured consistent performance across varying speaker energy levels.
Achieved substantial improvements in both pitch estimation and speaker assignment accuracy.

Conclusions:

Deep neural networks offer a powerful approach for complex multipitch tracking tasks.
The developed methods provide a significant advancement in simultaneous speech processing.
The system demonstrates robustness and high performance in realistic two-speaker scenarios.