Predicting the Perceived Level of Reverberation using Features from Nonlinear Auditory Model

Autor: Saeid Safavi, Wenwu Wang, Mark Plumbley, Ali Janalizadeh Choobbasti, George Fazekas

Publikováno v: Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 602, Iss 23, Pp 527-531 (2018)

Perceptual measurements have typically been recog- nized as the most reliable measurements in assessing perceived levels of reverberation. In this paper, a combination of blind RT60 estimation method and a binaural, nonlinear auditory model is employ

Externí odkaz: https://doaj.org/article/4fdffc4bd53f483583f195c2b6bc1bf0

On Metric Learning for Audio-Text Cross-Modal Retrieval

Autor: Xinhao Mei, Xubo Liu, Jianyuan Sun, Mark Plumbley, Wenwu Wang

Audio-text retrieval aims at retrieving a target audio clip or caption from a pool of candidates given a query in another modality. Solving such cross-modal retrieval task is challenging because it not only requires learning robust feature representa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fba773bd68da79e7e069fdf24599b82
http://arxiv.org/abs/2203.15537

Attention-based Convolutional Neural Networks for Acoustic Scene Classification

Autor: Ren, Zhao, Kong, Qiuqiang, Qian, Kun, Mark Plumbley, Schuller, Björn

Publikováno v: ResearcherID

We propose a convolutional neural network (CNN) model based on an attention pooling method to classify ten different acoustic scenes, participating in the acoustic scene classification task of the IEEE AASPChallengeonDetectionandClassificationofAco

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69665e7df54a141fb34b395d3881fc29

SiSEC 2018: State of the art in musical audio source separation - subjective selection of the best algorithm

Autor: Ward, Dominic, Mason, Russell D., Kim, Ryan Chungeun, Stöter, Fabian-Robert, Liutkus, Antoine, Mark Plumbley

Publikováno v: 4th Workshop on Intelligent Music Production
WIMP: Workshop on Intelligent Music Production
WIMP: Workshop on Intelligent Music Production, Sep 2018, Huddersfield, United Kingdom
ResearcherID

International audience; The Signal Separation Evaluation Campaign (SiSEC) is a large-scale regular event aimed at evaluating current progress in source separation through a systematic and reproducible comparison of the participants’ algorithms, pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::3c6e886c6cbc756587f792a28f439dda
https://inria.hal.science/hal-01945362

Fast Iterative Shrinkage for Signal Declipping and Dequantization

Autor: Rencker, Lucas, Bach, Francis, Wang, Wenwu, Mark Plumbley

Publikováno v: ResearcherID

We address the problem of recovering a sparse signal from clipped or quantized measurements. We show how these two problems can be formulated as minimizing the distance to a convex feasibility set, which provides a convex and differentiable cost func

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::928575fc92d1e91b579168cb9edf59b7