Zobrazeno 1 - 10
of 843
pro vyhledávání: '"sound event detection"'
Autor:
Maxim K. Surkov
Publikováno v:
Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki, Vol 24, Iss 5, Pp 758-769 (2024)
The task of automatic metainformation recognition from audio sources is to detect and extract data of various natures (speech, noises, acoustic scenes, acoustic events, anomalies) from a given audio input signal. This area is well developed and kno
Externí odkaz:
https://doaj.org/article/bf33ba510ce8430dbea5a9ae18304a00
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-16 (2024)
Abstract Polyphonic sound source localization and detection (SSLD) task aims to recognize the categories of sound events, identify their onset and offset times, and detect their corresponding direction-of-arrival (DOA), where polyphonic refers to the
Externí odkaz:
https://doaj.org/article/b5fbdcd2e5e04128a8e197a9fe9d9aa0
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5653-5668 (2024)
Abstract Sound event detection involves identifying sound categories in audio and determining when they start and end. However, in real-life situations, sound events are usually not isolated. When one sound event occurs, there are often other related
Externí odkaz:
https://doaj.org/article/f5468eb5010046d8ae1ec0da5a79e1d8
Autor:
Michael Nigro, Sridhar Krishnan
Publikováno v:
Machine Learning with Applications, Vol 18, Iss , Pp 100593- (2024)
Audio scene analysis involves a variety of tasks to obtain information from an audio environment. Audio source counting is one such task that has implications to many other aspects of audio analysis, yet it is relatively unexplored. This work present
Externí odkaz:
https://doaj.org/article/ab4d25579a8345c9ae0c15f10fbd76bb
Publikováno v:
Frontiers in Bird Science, Vol 3 (2024)
In the context of passive acoustic monitoring (PAM) better models are needed to reliably gain insights from large amounts of raw, unlabeled data. Bioacoustics foundation models, which are general-purpose, adaptable models that can be used for a wide
Externí odkaz:
https://doaj.org/article/c7c831c2007441be83cc3f804a15e53c
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 294-302 (2024)
Sound event detection systems are widely used in various applications such as surveillance and environmental monitoring where data is automatically collected, processed, and sent to a cloud for sound recognition. However, this process may inadvertent
Externí odkaz:
https://doaj.org/article/2885b88b78c14f33afdd999ce2585205
Publikováno v:
Sensors, Vol 24, Iss 16, p 5336 (2024)
Sound Event Detection and Localization (SELD) is a comprehensive task that aims to solve the subtasks of Sound Event Detection (SED) and Sound Source Localization (SSL) simultaneously. The task of SELD lies in the need to solve both sound recognition
Externí odkaz:
https://doaj.org/article/eeb3b677ce764ca886ec72137175c18a
Autor:
Marcelo Araya‐Salas, Grace Smith‐Vidaurre, Gloriana Chaverri, Juan C. Brenes, Fabiola Chirino, Jorge Elizondo‐Calvo, Alejandro Rico‐Guevara
Publikováno v:
Methods in Ecology and Evolution, Vol 14, Iss 9, Pp 2259-2271 (2023)
Abstract Animal acoustic signals are widely used in diverse research areas due to the relative ease with which sounds can be registered across a wide range of taxonomic groups and research settings. However, bioacoustics research can quickly generate
Externí odkaz:
https://doaj.org/article/3a68ebac31c64f2982c5dfbc2e4b9d81
Autor:
Yuting Zhou, Hongjie Wan
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2023, Iss 1, Pp 1-15 (2023)
Abstract The goal of sound event detection and localization (SELD) is to identify each individual sound event class and its activity time from a piece of audio, while estimating its spatial location at the time of activity. Conformer combines the adv
Externí odkaz:
https://doaj.org/article/d61bc4c192794481983ba08b224b34df
Autor:
Rhoddy Viveros-Muñoz, Pablo Huijse, Victor Vargas, Diego Espejo, Victor Poblete, Jorge P. Arenas, Matthieu Vernier, Diego Vergara, Enrique Suárez
Publikováno v:
Data in Brief, Vol 50, Iss , Pp 109552- (2023)
This paper presents the Synthetic Polyphonic Ambient Sound Source (SPASS) dataset, a publicly available synthetic polyphonic audio dataset. SPASS was designed to train deep neural networks effectively for polyphonic sound event detection (PSED) in ur
Externí odkaz:
https://doaj.org/article/8d570cd5eff344b48d3f24f256ee4296