IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events MULTIRESOLUTION AUDITORY REPRESENTATIONS FOR SCENE CLASSIFICATION
نویسندگان
چکیده
Here, we propose a framework that provides a detailed analysis of the spectrotemporal modulations in the acoustic signal, augmented with a discriminative classifier using support vector machines. We have seen that such representation is successful at capturing the nontrivial commonalties within a sound class and differences between different classes[1, 2, 3].
منابع مشابه
Deep Sequential Image Features for Acoustic Scene Classification
For the Acoustic Scene Classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2017), we propose a novel method to classify 15 different acoustic scenes using deep sequential learning, based on features extracted from Short-Time Fourier Transform and scalogram of the audio scenes using Convolutional Neural Networks. It is the first time...
متن کاملIEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events RECOGNISING ACOUSTIC SCENES WITH LARGE-SCALE AUDIO FEATURE EXTRACTION AND SVM
This work describes our contribution to the IEEE AASP Challenge on classification of acoustic scenes. From the 30 second long highly variable recordings, spectral, cepstral, energy and voicing-related audio features are extracted. A sliding window approach is used to obtain statistical functionals of the low-level features on short segments. SVM are used for classification of these short segmen...
متن کاملPairwise Decomposition with Deep Neural Networks and Multiscale Kernel Subspace Learning for Acoustic Scene Classification
We propose a system for acoustic scene classification using pairwise decomposition with deep neural networks and dimensionality reduction by multiscale kernel subspace learning. It is our contribution to the Acoustic Scene Classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2016). The system classifies 15 different acoustic scenes. ...
متن کاملIEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events AN EXEMPLAR-BASED NMF APPROACH FOR AUDIO EVENT DETECTION
We present a novel, exemplar-based method for audio event detection based on non-negative matrix factorisation (NMF). Building on recent work in noise robust automatic speech recognition, we model events as a linear combination of dictionary atoms, and mixtures as a linear combination of overlapping events. The exemplarbased dictionary is created by extracting all available training data, artif...
متن کاملIEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events AN I-VECTOR BASED APPROACH FOR AUDIO SCENE DETECTION
The IEEE-ASSP Scene Classification challenge on user-generated content (UGC) aims to classify an audio recording that belongs to a specific scene such as busystreet, office or supermarket. The difficulty of scene content analysis on UGC lies in the lack of structure and acoustic variability of the data. The i-vector system is state-ofthe-art in Speaker Verification and Scene Detection, and is o...
متن کامل