With the ever-rising quality of deep generative models,it is increasingly important to be able to discern whether theaudio data at hand have been recorded or synthesized. Althoughthe detection of fake speech signals has been studied extensively,this is not the case for the detection of fake environmental audio.We propose a simple and efficient pipeline for detecting… Continue reading Detection of Deepfake Environmental Audio @ EUSIPCO
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Sound source classification for soundscape analysis using fast third-octave bands data from an urban acoustic sensor network @ JASA
The exploration of the soundscape relies strongly on the characterization of the sound sources in the sound environment. Novel sound source classifiers, called pre-trained audio neural networks (PANNs), are capable of predicting the presence of more than 500 diverse sound sources. Nevertheless, PANNs models use fine Mel spectro-temporal representations as input, whereas sensors of an… Continue reading Sound source classification for soundscape analysis using fast third-octave bands data from an urban acoustic sensor network @ JASA
EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal @ CBMI
In this paper, we introduce the Extreme Metal Vocals Dataset, which comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music. The dataset consists of 760 audio excerpts of 1 second to 30 seconds long, totaling about 100 min of audio material, roughly composed of 60 minutes of… Continue reading EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal @ CBMI
Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependent @ EUSIPCO
This paper explores whether considering alternative domain-specific embeddings to calculate the Frechet Audio Dis- tance (FAD) metric can help the FAD to correlate better with perceptual ratings of environmental sounds. We used embeddings from VGGish, PANNs, MS-CLAP, L-CLAP, and MERT, which are tailored for either music or environmental sound evaluation. The FAD scores were calculated… Continue reading Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependent @ EUSIPCO
On the Robustness of Musical Timbre Perception Models: From Perceptual to Learned Approaches @ EUSIPCO
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.
Journée GdR IASIS “Synthèse audio” à l’Ircam
As part of the CNRS special interest group on signal and image processing (GdR IASIS), we are organizing a 1-day workshop on audio synthesis at Ircam on November 7th, 2024.
Dans le cadre de l’axe « Audio, Vision, Perception » du GdR IASIS, nous organisons une journée d’études dédiée à la synthèse audio. La journée se tiendra le jeudi 7 novembre 2024 à l’Ircam (laboratoire STMS), à Paris.
Content-Based Indexing for Audio and Music: From Analysis to Synthesis
Audio has long been a key component of multimedia research. As far as indexing is concerned, the research and industrial context has changed drastically in the last 20 years or so. Today, applications of audio indexing range from karaoke applications to singing voice synthesis and creative audio design. This special session aims at bringing together researchers that aim at proposing new tools or paradigms to investigate audio and music processing in the context of indexation and corpus-based generation.
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.