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Subband modeling for spoofing detection in automatic speaker verification
(ISCA, 2020-11-01)
Spectrograms - time-frequency representations of audio signals - have found widespread use in neural network-based spoofing detection. While deep models are trained on the fullband spectrum of the signal, we argue that not ...
Learning and Evaluation Methodologies for Polyphonic Music Sequence Prediction with LSTMs
(Institute of Electrical and Electronics Engineers, 2020)
Music language models (MLMs) play an important role for various music signal and symbolic music processing tasks, such as music generation, symbolic music classification, or automatic music transcription (AMT). In this ...
Modeling plate and spring reverberation using a DSP-informed deep neural network
(IEEE, 2020-05-04)
Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation. Currently, they are often used in music production for aesthetic reasons due to their ...
Deep Learning for Black-Box Modeling of Audio Effects
(MDPI AG, 2020-01-16)
Virtual analog modeling of audio effects consists of emulating the sound of an audio processor reference device. This digital simulation is normally done by designing mathematical models of these systems. It is often ...
A Study on the Transferability of Adversarial Attacks in Sound Event Classification
(IEEE, 2020-05-04)
An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. ...
Audio impairment recognition using a correlation-based feature representation
(IEEE, 2020-05-26)
Audio impairment recognition is based on finding noise in audio files and categorising the impairment type. Recently, significant performance improvement has been obtained thanks to the usage of advanced deep learning ...
Playing Technique Recognition by Joint Time–Frequency Scattering
(IEEE, 2020-05-04)
Playing techniques are important expressive elements in music signals. In this paper, we propose a recognition system based on the joint time–frequency scattering transform (jTFST) for pitch evolution-based playing techniques ...
A-CRNN: a domain adaptation model for sound event detection
(IEEE, 2020-05-04)
This paper presents a domain adaptation model for sound event detection. A common challenge for sound event detection is how to deal with the mismatch among different datasets. Typically, the performance of a model will ...
Reliable Local Explanations for Machine Listening
(IEEE, 2020-07-19)
One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions. Sensitivity analysis, which involves analysing the effect ...
Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation
(Institute of Electrical and Electronics Engineers, 2020)
This paper addresses the problem of domain adaptation for the task of music source separation. Using datasets from two different domains, we compare the performance of a deep learning-based harmonic-percussive source ...