| Paper ID | E-3-1.5 |
| Paper Title |
IMPACT OF MINIMUM HYPERSPHERICAL ENERGY REGULARIZATION ON TIME-FREQUENCY DOMAIN NETWORKS FOR SINGING VOICE SEPARATION |
| Authors |
Neil Shah, Dharmeshkumar Agrawal, TCS Research, Tata Consultancy Services Pvt. Ltd., Pune, India, India |
| Session |
E-3-1: Speech Separation 1 |
| Time | Thursday, 10 December, 12:30 - 14:00 |
| Presentation Time: | Thursday, 10 December, 13:30 - 13:45 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Speech, Language, and Audio (SLA): |
| Abstract |
The task of singing voice separation requires the model to maintain a trade-off between signal quality, interference introduced by music accompaniment and algorithmic artifacts. A time domain-based singing voice separation system offers a challenge in designing for low latency and in minimizing computational cost. To overcome this problem, we propose to use Gammatone auditory features for the Time-Frequency (T-F) mask-based singing voice separation task. Minimum Hyperspherical Energy (MHE) regularization in the time-domain network has recently produced the state-of-the-art result in singing voice separation (our baseline). In this work, we apply MHE to the T-F domain networks. The MHE regularized T-F domain network significantly improves the separation performance over the baseline. The MHE regularized Wasserstein Generative Adversarial Network (GAN) achieves 0.21 dB improvement in mean Signal-to-Distortion Ratio (SDR) over the baseline. Our best performing T-F domain un-regularized GAN provides an improvement of 0.75 dB and 0.63 dB in SDR over the baseline and the GAN-MHE, respectively. We experimentally show the failure of MHE regularized T-F domain networks with respect to their un-regularized versions and have shown the need of designing a suitable adversarial objective function. We report that modifying the GAN-MHE's objective function with reconstruction loss and adapting Wasserstein GAN, results in a 0.45 dB improvement in mean SDR over its un-regularized version. |