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Found 8 papers, 5 papers with code
Fitting Auditory Filterbanks with Multiresolution Neural Networks
Waveform-based deep learning faces a dilemma between nonparametric and parametric approaches.
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.
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.
Sound Event Detection in Synthetic Audio: Analysis of the DCASE 2016 Task Results
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds.
An evaluation framework for event detection using a morphological model of acoustic scenes
This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes.
The bag-of-frames approach: a not so sufficient model for urban soundscapes
The "bag-of-frames" approach (BOF), which encodes audio signals as the long-term statistical distribution of short-term spectral features, is commonly regarded as an effective and sufficient way to represent environmental sound recordings (soundscapes) since its introduction in an influential 2007 article.
