T. Heittola

Publications76
h-index25
Citations4,192
Highly Influential Citations396
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DCASE2017 Challenge Setup: Tasks, Datasets and Baseline System
TLDR
This paper presents the setup of these tasks: task definition, dataset, experimental setup, and baseline system results on the development dataset.
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TUT database for acoustic scene classification and sound event detection
TLDR
The recording and annotation procedure, the database content, a recommended cross-validation setup and performance of supervised acoustic scene classification system and event detection baseline system using mel frequency cepstral coefficients and Gaussian mixture models are presented.
View on IEEE
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
TLDR
This work combines these two approaches in a convolutional recurrent neural network (CRNN) and applies it on a polyphonic sound event detection task and observes a considerable improvement for four different datasets consisting of everyday sound events.
View on ACM
Metrics for Polyphonic Sound Event Detection
This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources…
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A multi-device dataset for urban acoustic scene classification
TLDR
The acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task are introduced, and the performance of a baseline system in the task is evaluated.
View PDF on arXiv
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.
View on ACM
Polyphonic sound event detection using multi label deep neural networks
TLDR
Frame-wise spectral-domain features are used as inputs to train a deep neural network for multi label classification in this work and the proposed method improves the accuracy by 19% percentage points overall.
View on IEEE
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
TLDR
The proposed SED system is compared against the state of the art mono channel method on the development subset of TUT sound events detection 2016 database and the usage of spatial and harmonic features are shown to improve the performance of SED.
View PDF on arXiv
Acoustic event detection in real life recordings
TLDR
A system for acoustic event detection in recordings from real life environments using a network of hidden Markov models, capable of recognizing almost one third of the events, and the temporal positioning of the Events is not correct for 84% of the time.
View on IEEE
Musical Instrument Recognition in Polyphonic Audio Using Source-Filter Model for Sound Separation
This paper proposes a novel approach to musical instrument recognition in polyphonic audio signals by using a source-filter model and an augmented non-negative matrix factorization algorithm for…
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