The Best 23 Sound Separation Tools in 2025

This is a ConvTasNet model trained based on the Asteroid framework, specifically designed for audio separation tasks, trained on the sep_clean task of the Libri2Mix dataset.

An audio source separation model based on the SepFormer architecture, trained on the WSJ0-2Mix dataset, capable of separating mixed audio into independent speech sources.

ConvTasNet model trained on the Asteroid framework for noisy speech separation tasks, trained on the Libri2Mix dataset.

This is an audio source separation model based on the SepFormer architecture, trained on the WHAMR! dataset, suitable for separating audio signals at a 16kHz sampling rate.

This is an audio source separation model based on the SepFormer architecture, trained on the WHAM! dataset, capable of separating different sound sources from mixed audio.

SepFormer is a Transformer-based audio source separation model trained on the WHAMR! dataset, designed to separate mixed speech signals.

This is an audio source separation model based on the SepFormer architecture, trained on the Libri3Mix dataset, capable of separating mixed speech into multiple independent sound sources.

TIGER is a lightweight speech separation model that effectively extracts key acoustic features through frequency band partitioning, multi-scale, and full-band frame modeling.

Audio source separation model implemented with SepFormer architecture, trained on the Libri2Mix dataset, capable of separating independent sound sources from mixed audio

A speech separation model trained on the Asteroid framework using the WHAM! dataset, focusing on clean speech separation tasks.

This is an audio source separation model based on the RE-SepFormer architecture, implemented by SpeechBrain and trained on the WSJ0-2Mix dataset.

ConvTasNet model trained based on the Asteroid framework for separating noise and speech signals from mixed audio.

This is a ConvTasNet model trained based on the Asteroid framework, specifically designed for audio separation tasks, trained on the sep_clean task of the WHAM! dataset.

ConvTasNet model trained on the Asteroid framework for speech separation tasks at 8kHz sampling rate, trained on the Libri2Mix dataset.

This is an audio source separation model using the SepFormer architecture, trained on the WSJ0-3Mix dataset, capable of separating mixed speech into independent speech sources.

TIGER is a lightweight speech separation model that achieves efficient audio processing through frequency band segmentation and multi-scale feature extraction

This is an audio source separation model trained based on the Asteroid framework, specifically designed to extract clean speech signals from mixed audio.

A ConvTasNet model trained on the Asteroid framework for speech separation tasks, trained on the Libri3Mix dataset, supporting 16kHz sample rate audio input.

A ConvTasNet model trained based on the Asteroid framework, designed to separate 3 independent audio sources from mixed audio, specifically optimized for noisy speech data at 8kHz sampling rate.

ConvTasNet model trained on the Asteroid framework for speech separation tasks at 8kHz sampling rate, specifically designed for noisy mixed speech scenarios.

Audio source separation model trained on the Asteroid framework, optimized for speech separation tasks at 8kHz sampling rate

ConvTasNet model trained on the Asteroid framework for noisy audio separation tasks, with training data from the Libri3Mix dataset.

An audio separation model trained based on the Asteroid framework, specifically designed for multi-channel audio signal separation tasks with noise