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Found 8 papers, 2 papers with code
SEFGAN: Harvesting the Power of Normalizing Flows and GANs for Efficient High-Quality Speech Enhancement
This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum likelihood training and Generative Adversarial Networks (GANs) for efficient speech enhancement (SE).
Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio
When individual audio stems are unavailable from production, Dialogue Separation (DS) can be applied to the final audio mixture to obtain estimates of these stems.
Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation
Deep generative models for Speech Enhancement (SE) received increasing attention in recent years.
A DNN Based Post-Filter to Enhance the Quality of Coded Speech in MDCT Domain
Frequency domain processing, and in particular the use of Modified Discrete Cosine Transform (MDCT), is the most widespread approach to audio coding.
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation
The music separation models are selected as they share the number of channels (2) and sampling rate (44. 1 kHz or higher) with the considered broadcast content, and vocals separation in music is considered as a parallel for dialog separation in the target application domain.
A Flow-Based Neural Network for Time Domain Speech Enhancement
Speech enhancement involves the distinction of a target speech signal from an intrusive background.
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count Estimation
Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance.
Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation
The task of estimating the maximum number of concurrent speakers from single channel mixtures is important for various audio-based applications, such as blind source separation, speaker diarisation, audio surveillance or auditory scene classification.
Audio and Speech Processing Sound
