Acoustic Event Classification Using Deep Neural Networks
Tutkimustuotos: Diplomityö tai pro gradu -työ ›
|Kustantaja||Tampere University of Technology|
|Tila||Julkaistu - 5 helmikuuta 2014|
|OKM-julkaisutyyppi||G2 Pro gradu, diplomityö, ylempi amk-opinnäytetyö|
In this thesis, effects of several NN classifier parameters such as number of hidden layers, number of units in hidden layers, batch size, learning rate etc. on classification accuracy are examined. Effects of implementation parameters such as types of features, number of adjacent frames, number of most energetic frames etc. are also investigated. A classification accuracy of 61.1% has been achieved with certain parameter values. In the case of DBNs, An application of greedy, layer-wise, unsupervised training before standard supervised training in order to initialize network weights in a better way, provided a 2-4% improvement in classification performance. A NN that had randomly initialized weights before supervised training was shown to be considerably powerful in terms of acoustic event classification tasks compared to conventional methods. DBNs have provided even better classification accuracies and justified its significant potential for further research on the topic.