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Found 8 papers, 4 papers with code
Reliable Local Explanations for Machine Listening
One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions.
GAN-based Generation and Automatic Selection of Explanations for Neural Networks
However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow.
Ensemble Models for Spoofing Detection in Automatic Speaker Verification
Our ensemble model outperforms all our single models and the baselines from the challenge for both attack types.
Audio and Speech Processing Sound
The "Horse'' Inside: Seeking Causes Behind the Behaviours of Music Content Analysis Systems
We perform a series of experiments to illuminate what the system has actually learned to do, and to what extent it is performing the intended music listening task.
Music transcription modelling and composition using deep learning
We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition.
Deep Learning and Music Adversaries
An adversary is essentially an algorithm intent on making a classification system perform in some particular way given an input, e. g., increase the probability of a false negative.
On Evaluation Validity in Music Autotagging
Specifically, does the figure of merit, or a comparison of figures of merit, warrant a conclusion about how well autotagging systems have learned to describe music with a specific vocabulary?
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR).
Sound
