71 skills found · Page 1 of 3
DCASE-REPO / Dcase UtilA collection of utilities for Detection and Classification of Acoustic Scenes and Events
doans / Underwater Acoustic Target Classification Based On Dense Convolutional Neural NetworkIn oceanic remote sensing operations, underwater acoustic target recognition is always a difficult and extremely important task of sonar systems, especially in the condition of complex sound wave propagation characteristics. Expensively learning recognition model for big data analysis is typically an obstacle for most traditional machine learning (ML) algorithms, whereas convolutional neural network (CNN), a type of deep neural network, can automatically extract features for accurate classification. In this study, we propose an approach using a dense CNN model for underwater target recognition. The network architecture is designed to cleverly re-use all former feature maps to optimize classification rate under various impaired conditions while satisfying low computational cost. In addition, instead of using time-frequency spectrogram images, the proposed scheme allows directly utilizing original audio signal in time domain as the network input data. Based on the experimental results evaluated on the real-world dataset of passive sonar, our classification model achieves the overall accuracy of 98.85$\%$ at 0 dB signal-to-noise ratio (SNR) and outperforms traditional ML techniques, as well as other state-of-the-art CNN models.
huckiyang / Voice2Series ReprogrammingICML 21 - Voice2Series: Adversarial Reprogramming Acoustic Models for Time Series Classification
kahst / AcousticEventDetectionSource code complementing our paper for acoustic event classification using convolutional neural networks.
CHeggan / MetaAudio A Few Shot Audio Classification BenchmarkA new comprehensive and diverse few-shot acoustic classification benchmark.
Jungjee / DcaseNetAuthor's repository for reproducing DcaseNet, an integrated pre-trained DNN that performs acoustic scene classification, audio tagging, and sound event detection. Implemented using PyTorch.
mohaimenz / AcdnetOfficial repository: Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
toni-heittola / Icassp2019 TutorialICASSP2019 Tutorial: Detection and Classification of Acoustic Scenes and Events / Code examples
pddpauw / BirdPiA realtime acoustic bird classification system for the Raspberry Pi 5, based on BirdNET-Pi
karolpiczak / Paper 2017 DCASEThe details that matter: Frequency resolution of spectrograms in acoustic scene classification - paper replication data
umer-sheikh / Bird Whisperer[InterSpeech 2024] Official code repository of paper titled "Bird Whisperer: Leveraging Large Pre-trained Acoustic Model for Bird Call Classification" accepted in InterSpeech 2024 conference.
shayangharib / AUDASCAdversarial Unsupervised Domain Adaptation for Acoustic Scene Classification
WangHelin1997 / SpecAugment PlusA Pytorch implementation of the paper : SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification
eddardd / WBTransport(ICASSP'21/CVPR'21) Wasserstein Barycenter Transport
zakaria76al / USCThe official implementation of the paper "A spatio-temporal deep learning approach for underwater acoustic signals classification". In this repository, we present two new deep learning architectures based on spatio-temporal modeling for underwater signal classification.
lucascesarfd / Underwater SndOfficial implementation of the paper "An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data"
fschmid56 / Cpjku Dcase23This repository contains the code of the CP JKU submission to DCASE23 Task 1 "Low-complexity Acoustic Scene Classification"
Hadryan / TFNet For Environmental Sound ClassificationLearning discriminative and robust time-frequency representations for environmental sound classification: Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, attention mechanisms have been used in CNN to capture the useful information from the audio signal for sound classification, especially for weakly labelled data where the timing information about the acoustic events is not available in the training data, apart from the availability of sound class labels. In these methods, however, the inherent time-frequency characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a new method, called time-frequency enhancement block (TFBlock), which temporal attention and frequency attention are employed to enhance the features from relevant frames and frequency bands. Compared with other attention mechanisms, in our method, parallel branches are constructed which allow the temporal and frequency features to be attended respectively in order to mitigate interference from the sections where no sound events happened in the acoustic environments. The experiments on three benchmark ESC datasets show that our method improves the classification performance and also exhibits robustness to noise.
soundclim / AnurasetAnuraSet: A dataset for classification of tropical anurans from passive acoustic monitoring
Wangkkklll / DSMN Dcase2023[DCASE 2023] Official Implementation for "Low-Complexity Acoustic Scene Classification Using Deep Space Separable Distillation And Mutil-Task Learning"
