Results 121 to 130 of about 4,447 (146)

Polyphonic Sound Event Detection Using Transposed Convolutional Recurrent Neural Network

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
In this paper we propose a Transposed Convolutional Recurrent Neural Network (TCRNN) architecture for polyphonic sound event recognition. Transposed convolution layer, which caries out a regular convolution operation but reverts the spatial transformation and it is combined with a bidirectional Recurrent Neural Network (RNN) to get TCRNN.
Chandra Churh Chatterjee, Manjunath Mulimani, Shashidhar G. Koolagudi   +2 more
openaire   +3 more sources

Augmented Strategy For Polyphonic Sound Event Detection

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019
Sound event detection is an important issue for many applications like audio content retrieval, intelligent monitoring, and scene-based interaction. The traditional studies on this topic are mainly focusing on identification of single sound event class.
Bolun Wang, Zhong-Hua Fu, Hao Wu
openaire   +1 more source

Polyphonic Sound Event Detection with Weak Labeling

2023
 Sound event detection (SED) is the task of detecting the type as well as the onset and offset times of sound events in audio streams. It is useful for multimedia retrieval, surveillance, etc. SED is difficult because sound events exhibit diverse temporal and spectral characteristics, and because they can overlap with each other.
openaire   +1 more source

Polyphonic sound event detection using multi label deep neural networks

2015 International Joint Conference on Neural Networks (IJCNN), 2015
In this paper, the use of multi label neural networks are proposed for detection of temporally overlapping sound events in realistic environments. Real-life sound recordings typically have many overlapping sound events, making it hard to recognize each event with the standard sound event detection methods.
Cakir Emre, Huttunen Heikki, Heittola Toni, Virtanen Tuomas   +3 more
openaire   +1 more source

Relational recurrent neural networks for polyphonic sound event detection

Multimedia Tools and Applications, 2019
A smart environment is one of the application scenarios of the Internet of Things (IoT). In order to provide a ubiquitous smart environment for humans, a variety of technologies are developed. In a smart environment system, sound event detection is one of the fundamental technologies, which can automatically sense sound changes in the environment and ...
Junbo Ma, Ruili Wang, Wanting Ji, Hao Zheng, En Zhu, Jianping Yin   +5 more
openaire   +1 more source

Complex Activity Recognition Using Polyphonic Sound Event Detection

2018
In this paper, we propose a method for recognizing the complex activity using audio sensors and the machine learning techniques. To do so, we will look for the patterns of combined monophonic sounds to recognize complex activity. At this time, we use only audio sensors and the machine learning techniques like Deep Neural Network (DNN) and Support ...
Jaewoong Kang, Jooyeong Kim, Kunyoung Kim, Mye Sohn   +3 more
openaire   +1 more source

A survey of deep learning for polyphonic sound event detection

2017 International Conference on Orange Technologies (ICOT), 2017
Deep learning has achieved state of the art in various machine learning problems, such as computer vision, speech recognition, and natural language processing. Sound event detection (SED), which is about recognizing audio events in real-life environments, has attracted a lot of attention recently.
An Dang, Toan H. Vu, Jia-Ching Wang
openaire   +1 more source

U Recurrent Neural Network for Polyphonic Sound Event Detection and Localization

Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing, 2020
The polyphonic sound event detection and localization (SELD) system indicates the temporal onset and offset time of sound events to be detected and tracks the spatial location of the acoustic source. It involves two processes, the sound event detection (SED) and the estimation of the direction of arrival (DOA).
Lihong Pi, Xue Zheng, Chun Zhang, Ping Chen, Zhe Wang, Xiangyu Li   +5 more
openaire   +1 more source

BLSTM-HMM hybrid system combined with sound activity detection network for polyphonic Sound Event Detection

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
This paper presents a new hybrid approach for polyphonic Sound Event Detection (SED) which incorporates a temporal structure modeling technique based on a hidden Markov model (HMM) with a frame-by-frame detection method based on a bidirectional long short-term memory (BLSTM) recurrent neural network (RNN).
Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Takaaki Hori, Jonathan Le Roux, Kazuya Takeda   +5 more
openaire   +1 more source

Event Specific Attention for Polyphonic Sound Event Detection

Interspeech 2021, 2021
Harshavardhan Sundar, Ming Sun, Chao Wang   +2 more
openaire   +1 more source