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Found 10 papers, 1 papers with code
Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations
Acoustic signal processing in the spherical harmonics domain (SHD) is an active research area that exploits the signals acquired by higher order microphone arrays.
Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint
Acoustic scene classification (ASC) is one of the most popular problems in the field of machine listening.
TASK3 DCASE2021 Challenge: Sound event localization and detection using squeeze-excitation residual CNNs
This year, it has been decided to study how this technique improves each of the datasets (last year only the MIC dataset was studied).
Direction of Arrival Estimation
Sound Event Localization and Detection
Squeeze-Excitation Convolutional Recurrent Neural Networks for Audio-Visual Scene Classification
The early fusion stage combines features resulting from the last convolutional block of the respective subnetworks at different time steps to feed a bidirectional recurrent structure.
Anomalous Sound Detection using unsupervised and semi-supervised autoencoders and gammatone audio representation
Anomalous sound detection (ASD) is, nowadays, one of the topical subjects in machine listening discipline.
Listen carefully and tell: an audio captioning system based on residual learning and gammatone audio representation
An automated audio captioning system has to be implemented as it accepts an audio as input and outputs as textual description, that is, the caption of the signal.
Acoustic Scene Classification with Squeeze-Excitation Residual Networks
The behavior of the block that implements such operators and, therefore, the entire neural network, can be modified depending on the input to the block, the established residual configurations and the selected non-linear activations.
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
This paper is aimed at poviding the audio recognition community with a carefully annotated dataset (https://zenodo. org/record/3689288) for FSL in an OSR context comprised of 1360 clips from 34 classes divided into pattern sounds} and unwanted sounds.
On the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification
The purpose of this paper is to analyze and discuss the performance of several residual block implementations within a state-of-the-art CNN-based architecture for end-to-end audio classification using raw audio waveforms.
CNN depth analysis with different channel inputs for Acoustic Scene Classification
Many state-of-the-art solutions are based on image classification frameworks and, as such, a 2D representation of the audio signal is considered for training these networks.
