Add to ORKGThis technical report describes the SurreyAudioTeam22s submission for DCASE 2022 ASC Task 1, Low-Complexity Acoustic Scene Classification (ASC). The task has two rules, (a) the ASC framework should have maximum 128K parameters, and (b) there should be a maximum of 30 millions multiply-accumulate operations (MACs) per inference. In this report, we present low-complexity systems for ASC that follow the rules intended for the task.Comment: Technical Report DCASE 2022 TASK 1. arXiv admin note: substantial text overlap with arXiv:2207.1152
In this project we have developed a low complexity model for acoustic scene classification; based o...
This work proposes bag-of-features deep learning models for acoustic scene classi?cation (ASC) – ide...
The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five audio classific...
This paper presents the details of Task 1A Acoustic Scene Classification in the DCASE 2021 Challenge...
This paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in DCASE 20...
We present a method to develop low-complexity convolu-tional neural networks (CNNs) for acoustic sce...
Predicting acoustic environment by analyzing and classifying sound recording of the scene is an emer...
In this paper, we present an acoustic scene classification framework based on a large-margin factori...
We propose a convolutional neural network (CNN) model based on an attention pooling method to classi...
Research has shown the efficacy of using convolutional neural networks (CNN) with audio spectrograms...
Replacing 2D-convolution operations by depth-wise separable time and frequency convolutions greatly ...
In this paper, we present the details of our proposed framework and solution for the DCASE 2019 Task...
Acoustic Scene Classification (ASC) is one of the core research problems in the field of Computation...
This paper presents a novel application of convolutional neural networks (CNNs) for the task of acou...
The goal of Acoustic Scene Classification (ASC) is to recognise the environment in which an audio w...
In this project we have developed a low complexity model for acoustic scene classification; based o...
This work proposes bag-of-features deep learning models for acoustic scene classi?cation (ASC) – ide...
The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five audio classific...
This paper presents the details of Task 1A Acoustic Scene Classification in the DCASE 2021 Challenge...
This paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in DCASE 20...
We present a method to develop low-complexity convolu-tional neural networks (CNNs) for acoustic sce...
Predicting acoustic environment by analyzing and classifying sound recording of the scene is an emer...
In this paper, we present an acoustic scene classification framework based on a large-margin factori...
We propose a convolutional neural network (CNN) model based on an attention pooling method to classi...
Research has shown the efficacy of using convolutional neural networks (CNN) with audio spectrograms...
Replacing 2D-convolution operations by depth-wise separable time and frequency convolutions greatly ...
In this paper, we present the details of our proposed framework and solution for the DCASE 2019 Task...
Acoustic Scene Classification (ASC) is one of the core research problems in the field of Computation...
This paper presents a novel application of convolutional neural networks (CNNs) for the task of acou...
The goal of Acoustic Scene Classification (ASC) is to recognise the environment in which an audio w...
In this project we have developed a low complexity model for acoustic scene classification; based o...
This work proposes bag-of-features deep learning models for acoustic scene classi?cation (ASC) – ide...
The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five audio classific...