Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Tuomas Oskari Virtanen"'
Autor:
Irene Martín Morató, Francesco Paissan, Alberto Ancilotto, Toni Heittola, Annamaria Mesaros, Elisabetta Farella, Alessio Brutti, Tuomas Oskari Virtanen
Publikováno v:
Tampere University
This paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in DCASE 2022 Challenge. The task was a continuation from the previous years, but the low-complexity requirements were changed to the following: the maximum numb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94de677780da78e914d2dc2467e050d8
https://trepo.tuni.fi/handle/10024/145602
https://trepo.tuni.fi/handle/10024/145602
Publikováno v:
Tampere University
Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task 6, which aims at developing c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::201b66e9ccc4e8b535739c1f55de9737
https://trepo.tuni.fi/handle/10024/145603
https://trepo.tuni.fi/handle/10024/145603
Autor:
Archontis Politis, Kazuki Shimada, Parthasaarathy Ariyakulam Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Oskari Virtanen
Publikováno v:
Tampere University
This report presents the Sony-TAu Realistic Spatial Soundscapes 2022 (STARS22) dataset for sound event localization and detection, comprised of spatial recordings of real scenes collected in various interiors of two different sites. The dataset is ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c127d04abc8ca3e8f6fdf8cad5e2a34e
Publikováno v:
Tampere University
Audio representation learning based on deep neural networks (DNNs) emerged as an alternative approach to hand-crafted features. For achieving high performance, DNNs often need a large amount of annotated data which can be difficult and costly to obta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::781283319cd4d6e57fae6c51e831da0b
http://arxiv.org/abs/2006.08386
http://arxiv.org/abs/2006.08386
Publikováno v:
Tampere University
Audio captioning is the task of automatically creating a textual description for the contents of a general audio signal. Typical audio captioning methods rely on deep neural networks (DNNs), where the target of the DNN is to map the input audio seque
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bab6abb52fb1c567dbb3fab873c32256
https://trepo.tuni.fi/handle/10024/130103
https://trepo.tuni.fi/handle/10024/130103
Publikováno v:
Tampere University
Audio captioning is a multi-modal task, focusing on using natural language for describing the contents of general audio. Most audio captioning methods are based on deep neural networks, employing an encoder-decoder scheme and a dataset with audio cli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26b219456b508e93ca85acc12bcbfe1c
https://trepo.tuni.fi/handle/10024/130102
https://trepo.tuni.fi/handle/10024/130102
Publikováno v:
Tampere University
A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy. As a countermeasure, we present t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::182df030d86d40f0d3984a3872ee1e2b
Autor:
Konstantinos Drosos, Stylianos - Ioannis Mimilakis, Andreas Floros, Tuomas Oskari Virtanen, Gerald Schuller
Publikováno v:
Tampere University
Close miking represents a widely employed practice of placing a microphone very near to the sound source in order to capture more direct sound and minimize any pickup of ambient sound, including other, concurrently active sources. It is used by the a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90abbf1733b91ca21606e060a5065f03
Autor:
Miroslav Malik, Sharath Adavanne, Konstantinos Drosos, Tuomas Oskari Virtanen, Dasa Ticha, Roman Jarina
Publikováno v:
Scopus-Elsevier
Tampere University
Tampere University
This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a4444bd0d1cf059339688ed6c4c7b01
https://trepo.tuni.fi/handle/10024/129168
https://trepo.tuni.fi/handle/10024/129168
Publikováno v:
Tampere University
This paper proposes a neural network architecture and training scheme to learn the start and end time of sound events (strong labels) in an audio recording given just the list of sound events existing in the audio without time information (weak label
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::418b3015224bea3f3878304bb93a5976