Tag: murenn-project

HybrA @ ICA

“Hybrid auditory filterbanks (HybrA): Learnable, interpretable, and stable filterbanks for feature extraction”. A talk by Peter Balazs (Austrian Academy of Sciences) at International Congress on Acoustics in New Orleans. As part of the MuReNN project.

Residual Hybrid Filterbanks @ IEEE SSP

A hybrid filterbanks is a convolutional neural network (convnet) whose learnable filters operate over the subbands of a non-learnable filterbank, which is designed from domain knowledge. While hybrid filterbanks have found successful applications in speech enhancement, our paper shows that they remain susceptible to large deviations of the energy response due to randomness of convnet weights at initialization. Against this issue, we propose a variant of hybrid filterbanks, by inspiration from residual neural networks (ResNets). The key idea is to introduce a shortcut connection at the output of each non-learnable filter, bypassing the convnet. We prove that the shortcut connection in a residual hybrid filterbank lowers the relative standard deviation of the energy response while the pairwise cosine distances between non-learnable filters contributes to preventing duplicate features.

Robust Deconvolution with Parseval Filterbanks @ IEEE SampTA

This article introduces two contributions: Multiband Robust Deconvolution (Multi-RDCP), a regularization approach for deconvolution in the presence of noise; and Subband-Normalized Adaptive Kernel Evaluation (SNAKE), a first-order iterative algorithm designed to efficiently solve the resulting optimization problem. Multi-RDCP resembles Group LASSO in that it promotes sparsity across the subband spectrum of the solution. We prove that SNAKE enjoys fast convergence rates and numerical simulations illustrate the efficiency of SNAKE for deconvolving noisy oscillatory signals.

Robust Multicomponent Tracking of Ultrasonic Vocalizations @ IEEE ICASSP

Screenshot

Ultrasonic vocalizations (USV) convey information about individual identity and arousal status in mice. We propose to track USV as ridges in the time-frequency domain via a variant of timefrequency reassignment (TFR). The key idea is to perform TFR with empirical Wiener shrinkage and multitapering to improve robustness to noise. Furthermore, we perform TFR over both the short-term Fourier transform and the constant-Q transform so as to detect both the fundamental frequency and its harmonic partial (if any). Experimental results show that our approach effectively estimates multicomponent ridges with high precision and low frequency deviation.

Hold Me Tight: Stable Encoder–Decoder Design for Speech Enhancement

Convolutional layers with 1-D filters are often used as frontend to encode audio signals. Unlike fixed time-frequency representations, they can adapt to the local characteristics of input data. However, 1-D filters on raw audio are hard to train and often suffer from instabilities. In this paper, we address these problems with hybrid solutions, i.e., combining theory-driven and datadriven approaches. First, we preprocess the audio signals via a auditory filterbank, guaranteeing good frequency localization for the learned encoder. Second, we use results from frame theory to define an unsupervised learning objective that encourages energy conservation and perfect reconstruction. Third, we adapt mixed compressed spectral norms as learning objectives to the encoder coefficients. Using these solutions in a low-complexity encoder-mask-decoder model significantly improves the perceptual evaluation of speech quality (PESQ) in speech enhancement.

Machine Listening in a Neonatal Intensive Care Unit @ DCASE

Oxygenators, alarm devices, and footsteps are some of the most common sound sources in a hospital. Detecting them has scientific value for environmental psychology but comes with challenges of its own: namely, privacy preservation and limited labeled data. In this paper, we address these two challenges via a combination of edge computing and cloud computing. For privacy preservation, we have designed an acoustic sensor which computes third-octave spectrograms on the fly instead of recording audio waveforms. For sample-efficient machine learning, we have repurposed a pretrained audio neural network (PANN) via spectral transcoding and label space adaptation. A small-scale study in a neonatological intensive care unit (NICU) confirms that the time series of detected events align with another modality of measurement: i.e., electronic badges for parents and healthcare professionals. Hence, this paper demonstrates the feasibility of polyphonic machine listening in a hospital ward while guaranteeing privacy by design.

Trainable signal encoders that are robust against noise @ Inter-Noise

Within the deep learning paradigm, finite impulse response (FIR) filters are often used to encode audio signals, yielding flexible and adaptive feature representations. We show that a stabilization of FIR filterbanks with fixed filter lengths (convolutional layers with 1-D filters)leads to encoders that are optimally robust against noise and can be inverted with perfect reconstruction by their transposes. To maintain their flexibility as regular neural network layers, we implement the stabilization via a computationally efficient regularizing term in the objective function of the learning problem. In this way, the encoder keeps its expressive power and is optimally stable and noise-robust throughout the whole learning procedure. We show in a denoising task where noise is present in the input and in the encoder representation, that the proposed stabilization of the trainable filterbank encoder is decisive for increasing the signal-to-noise ratio of the denoised signals significantly compared to a model with a naively trained encoder.