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Found 18 papers, 14 papers with code
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others.
Instabilities in Convnets for Raw Audio
Numerical simulations align with our theory and suggest that the condition number of a convolutional layer follows a logarithmic scaling law between the number and length of the filters, which is reminiscent of discrete wavelet bases.
Fitting Auditory Filterbanks with Multiresolution Neural Networks
Waveform-based deep learning faces a dilemma between nonparametric and parametric approaches.
Few-shot bioacoustic event detection at the DCASE 2023 challenge
Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of interest.
Mesostructures: Beyond Spectrogram Loss in Differentiable Time-Frequency Analysis
Computer musicians refer to mesostructures as the intermediate levels of articulation between the microstructure of waveshapes and the macrostructure of musical forms.
Perceptual-Neural-Physical Sound Matching
On the other hand, mean square error in the spectrotemporal domain, known as "spectral loss", is perceptually motivated and serves in differentiable digital signal processing (DDSP).
Differentiable Time-Frequency Scattering on GPU
Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales.
Learning a Lie Algebra from Unlabeled Data Pairs
Deep convolutional networks (convnets) show a remarkable ability to learn disentangled representations.
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags.
Time-Frequency Scattering Accurately Models Auditory Similarities Between Instrumental Playing Techniques
Furthermore, it minimizes triplet loss in the cluster graph by means of the large-margin nearest neighbor (LMNN) metric learning algorithm.
wav2shape: Hearing the Shape of a Drum Machine
Disentangling and recovering physical attributes, such as shape and material, from a few waveform examples is a challenging inverse problem in audio signal processing, with numerous applications in musical acoustics as well as structural engineering.
One or Two Components? The Scattering Transform Answers
With the aim of constructing a biologically plausible model of machine listening, we study the representation of a multicomponent stationary signal by a wavelet scattering network.
Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization
This paper proposes to perform unsupervised detection of bioacoustic events by pooling the magnitudes of spectrogram frames after per-channel energy normalization (PCEN).
Learning the helix topology of musical pitch
To explain the consonance of octaves, music psychologists represent pitch as a helix where azimuth and axial coordinate correspond to pitch class and pitch height respectively.
Robust sound event detection in bioacoustic sensor networks
As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise.
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
Eigentriads and Eigenprogressions on the Tonnetz
We introduce a new multidimensional representation, named eigenprogression transform, that characterizes some essential patterns of Western tonal harmony while being equivariant to time shifts and pitch transpositions.
Sound Audio and Speech Processing
Wavelet Scattering on the Pitch Spiral
We present a new representation of harmonic sounds that linearizes the dynamics of pitch and spectral envelope, while remaining stable to deformations in the time-frequency plane.
