Speech enhancement is the task of taking a noisy speech input and producing an enhanced speech output.
( Image credit: A Fully Convolutional Neural Network For Speech Enhancement )
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We present and release a new tool for music source separation with pre-trained models called Spleeter. Spleeter was designed with ease of use, separation performance and speed in mind.
Ranked #8 on Music Source Separation on MUSDB18 (using extra training data)
To demonstrate this, we use the CHiME-6 Challenge data as an example of challenging environments and noisy conditions of everyday speech.
Ranked #1 on Speech Recognition on CHiME-6 dev_gss12
The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms.
Ranked #9 on Music Source Separation on MUSDB18
The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory.
Ranked #2 on Speech Enhancement on DEMAND
Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality.
SPEECH ENHANCEMENT AUDIO AND SPEECH PROCESSING SOUND
In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker.
In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.
The proposed model matches state-of-the-art performance of both causal and non causal methods while working directly on the raw waveform.
This paper investigates several aspects of training a RNN (recurrent neural network) that impact the objective and subjective quality of enhanced speech for real-time single-channel speech enhancement.
MMSE approaches utilising the proposed a priori SNR estimator are able to achieve higher enhanced speech quality and intelligibility scores than recent masking- and mapping-based deep learning approaches.