5,245 research outputs found
Online Monaural Speech Enhancement Using Delayed Subband LSTM
This paper proposes a delayed subband LSTM network for online monaural
(single-channel) speech enhancement. The proposed method is developed in the
short time Fourier transform (STFT) domain. Online processing requires
frame-by-frame signal reception and processing. A paramount feature of the
proposed method is that the same LSTM is used across frequencies, which
drastically reduces the number of network parameters, the amount of training
data and the computational burden. Training is performed in a subband manner:
the input consists of one frequency, together with a few context frequencies.
The network learns a speech-to-noise discriminative function relying on the
signal stationarity and on the local spectral pattern, based on which it
predicts a clean-speech mask at each frequency. To exploit future information,
i.e. look-ahead, we propose an output-delayed subband architecture, which
allows the unidirectional forward network to process a few future frames in
addition to the current frame. We leverage the proposed method to participate
to the DNS real-time speech enhancement challenge. Experiments with the DNS
dataset show that the proposed method achieves better performance-measuring
scores than the DNS baseline method, which learns the full-band spectra using a
gated recurrent unit network.Comment: Paper submitted to Interspeech 202
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders
We show that a Modular Neural Network (MNN) can combine various speech
enhancement modules, each of which is a Deep Neural Network (DNN) specialized
on a particular enhancement job. Differently from an ordinary ensemble
technique that averages variations in models, the propose MNN selects the best
module for the unseen test signal to produce a greedy ensemble. We see this as
Collaborative Deep Learning (CDL), because it can reuse various already-trained
DNN models without any further refining. In the proposed MNN selecting the best
module during run time is challenging. To this end, we employ a speech
AutoEncoder (AE) as an arbitrator, whose input and output are trained to be as
similar as possible if its input is clean speech. Therefore, the AE can gauge
the quality of the module-specific denoised result by seeing its AE
reconstruction error, e.g. low error means that the module output is similar to
clean speech. We propose an MNN structure with various modules that are
specialized on dealing with a specific noise type, gender, and input
Signal-to-Noise Ratio (SNR) value, and empirically prove that it almost always
works better than an arbitrarily chosen DNN module and sometimes as good as an
oracle result
Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect
We study the cocktail party problem and propose a novel attention network
called Tune-In, abbreviated for training under negative environments with
interference. It firstly learns two separate spaces of speaker-knowledge and
speech-stimuli based on a shared feature space, where a new block structure is
designed as the building block for all spaces, and then cooperatively solves
different tasks. Between the two spaces, information is cast towards each other
via a novel cross- and dual-attention mechanism, mimicking the bottom-up and
top-down processes of a human's cocktail party effect. It turns out that
substantially discriminative and generalizable speaker representations can be
learnt in severely interfered conditions via our self-supervised training. The
experimental results verify this seeming paradox. The learnt speaker embedding
has superior discriminative power than a standard speaker verification method;
meanwhile, Tune-In achieves remarkably better speech separation performances in
terms of SI-SNRi and SDRi consistently in all test modes, and especially at
lower memory and computational consumption, than state-of-the-art benchmark
systems.Comment: Accepted in AAAI 202
Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement
Deep learning (DL) networks have grown into powerful alternatives for speech enhancement and have achieved excellent results by improving speech quality, intelligibility, and background noise suppression. Due to high computational load, most of the DL models for speech enhancement are difficult to implement for realtime processing. It is challenging to formulate resource efficient and compact networks. In order to address this problem, we propose a resource efficient convolutional recurrent network to learn the complex ratio mask for real-time speech enhancement. Convolutional encoder-decoder and gated recurrent units (GRUs) are integrated into the Convolutional recurrent network architecture, thereby formulating a causal system appropriate for real-time speech processing. Parallel GRU grouping and efficient skipped connection techniques are engaged to achieve a compact network. In the proposed network, the causal encoder-decoder is composed of five convolutional (Conv2D) and deconvolutional (Deconv2D) layers. Leaky linear rectified unit (ReLU) is applied to all layers apart from the output layer where softplus activation to confine the network output to positive is utilized. Furthermore, batch normalization is adopted after every convolution (or deconvolution)
and prior to activation. In the proposed network, different noise types and speakers can be used in training and testing. With the LibriSpeech dataset, the experiments show that the proposed real-time approach leads to improved objective perceptual quality and intelligibility with much fewer trainable parameters than existing LSTM and GRU models. The proposed model obtained an average of 83.53% STOI scores and 2.52 PESQ scores, respectively. The quality and intelligibility are improved by 31.61% and 17.18% respectively over noisy speech
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