Jointly Recognizing Speech and Singing Voices in Mixed Audio Using Multi-Task Audio Source Separation

แนวคิดหลัก

A unified model called JRSV that jointly separates and recognizes speech and singing voices in mixed audio, outperforming cascade systems.

บทคัดย่อ

The paper proposes a unified model called JRSV (Jointly Recognizing Speech and singing Voices) to address the challenge of recognizing speech and singing voices in mixed audio.
The JRSV system consists of two main modules:

Multi-Task Audio Source Separation (MTASS) Module:

Separates the mixed audio into distinct speech and singing voice tracks, while also removing background music.
Employs a Conformer-based network with magnitude-based separation loss, discriminative separation loss, and consistency loss.

Automatic Speech Recognition (ASR) Module:

Uses a CTC/attention hybrid architecture to recognize the content of the separated speech and singing voice tracks.
Adopts online distillation to improve the robustness of the encoded representations.
Employs a two-stage training procedure, first training the MTASS module and then the ASR module.

The proposed JRSV system is evaluated on a new benchmark dataset called Dual-Track Speech and singing Voice Dataset (DTSVD), which contains mixed audio with varying overlap ratios between speech, singing voices, and background music.
The experimental results demonstrate that JRSV can significantly outperform a cascade system, achieving a relative reduction of 41% in character error rates (CERs) for speech and 57% in CER for singing voices on average.

สถิติ

The mixed audio signals are normalized and scaled with respect to sampled signal-to-noise ratios (SNRs) from uniform distributions: U(-10, 2) for speech and singing, and U(-15, 2) for background music.
The overlap ratio between speech and the mixed singing voice and music is randomly sampled from {1.0, 0.5, 0.3, 0.1, 0.0}.

คำพูด

"To achieve a detailed structured result of the speech and singing voices that mixed in audio, in this paper, we propose a unified model to Jointly Recognize Speech and singing Voices (JRSV)."
"Experimental results demonstrate that JRSV can significantly improve recognition accuracy on each track of the mixed audio."

ข้อมูลเชิงลึกที่สำคัญจาก

Jointly Recognizing Speech and Singing Voices Based on Multi-Task Audio Source Separation

by Ye Bai,Chenx... ที่ arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11275.pdf

สอบถามเพิ่มเติม

How could the JRSV model be extended to handle more than two audio sources (e.g., speech, singing, and sound effects) in the mixed audio

To extend the JRSV model to handle more than two audio sources in the mixed audio, such as speech, singing, and sound effects, the architecture can be modified to incorporate additional output layers in the MTASS module. Each output layer would correspond to a specific audio source, allowing the model to separate and recognize multiple types of audio tracks simultaneously. By adjusting the training process to include the new sources and updating the loss functions to account for the additional tracks, the JRSV model can be expanded to handle more complex audio mixtures effectively.

What are the potential limitations of the proposed online distillation approach, and how could it be further improved

The proposed online distillation approach in JRSV may have limitations in scenarios where the clean audio data is not readily available or when the model encounters highly variable acoustic conditions. To address these limitations, the online distillation process could be enhanced by incorporating adaptive learning rates to prioritize certain samples or sources during training. Additionally, introducing regularization techniques to prevent overfitting and exploring more sophisticated distillation strategies, such as knowledge distillation from multiple teacher models, could further improve the robustness and generalization of the model.

What other applications beyond speech and singing voice recognition could benefit from the multi-task audio source separation approach used in JRSV

Beyond speech and singing voice recognition, the multi-task audio source separation approach used in JRSV has applications in various fields such as audio content analysis, music transcription, and sound event detection. For instance, in audio content analysis, the ability to separate and identify different sound sources within a mixture can enhance the understanding and categorization of audio content. In music transcription, the model can be adapted to recognize individual instruments or vocals in music recordings, enabling more accurate and detailed music analysis. Moreover, in sound event detection, the multi-task audio source separation can help in isolating specific sounds of interest from complex audio environments, leading to improved detection and classification of sound events in real-world scenarios.