• Publications
  • Influence
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
TLDR
The emergence of deep learning as the most popular classification method is observed, replacing the traditional approaches based on Gaussian mixture models and support vector machines. Expand
Detection and classification of acoustic scenes and events: An IEEE AASP challenge
TLDR
An overview of systems submitted to the public evaluation challenge on acoustic scene classification and detection of sound events within a scene as well as a detailed evaluation of the results achieved by those systems are provided. Expand
A database and challenge for acoustic scene classification and event detection
TLDR
This paper introduces a newly-launched public evaluation challenge dealing with two closely related tasks of the field: acoustic scene classification and event detection. Expand
Detection and Classification of Acoustic Scenes and Events
TLDR
The state of the art in automatically classifying audio scenes, and automatically detecting and classifyingaudio events is reported on. Expand
Long Interpolation of Audio Signals Using Linear Prediction in Sinusoidal Modeling
Within the context of sinusoidal modeling, a new method for the interpolation of sinusoidal components is proposed. It is shown that autoregressive modeling of the amplitude and frequency parametersExpand
On automatic drum transcription using non-negative matrix deconvolution and itakura saito divergence
TLDR
New contributions for audio event detection methods using the Itakura Saito divergence are studied that improve efficiency and numerical stability, and simplify the generation of target pattern sets. Expand
Normalized Cuts for Predominant Melodic Source Separation
TLDR
This paper formulate predominant melodic source tracking and formation as a graph partitioning problem and solve it using the normalized cut which is a global criterion for segmenting graphs that has been used in computer vision. Expand
Multimodal similarity between musical streams for cover version detection
TLDR
It is shown that considering the mixture, an estimation of its main melody and its accompaniment as modalities allows the scoring system to focus more on the chord progression by considering the accompaniment while being robust to the potential separation errors by alsoConsidering the mixture. Expand
Uncertainty-based learning of acoustic models from noisy data
TLDR
A new expectation maximization (EM) based technique is introduced that allows us to train Gaussian mixture models (GMMs) or hidden Markov models (HMMs) directly from noisy data with dynamic uncertainty, and results in 3-4% absolute improvement in speaker recognition accuracy by training from either matched, unmatched or multi-condition noisy data. Expand
Sound Source Tracking and Formation using Normalized Cuts
TLDR
This work forms sound source tracking and formation as a graph partitioning problem and solves it using the normalized cut which is a global criterion for segmenting graphs that has been used in computer vision. Expand
...
1
2
3
4
5
...