TUT Audio Event Detection System 2007
نویسنده
چکیده
This paper describes a system used in acoustic event detection task of the CLEAR2007 evaluation. The objective of the task is to detect acoustic events (door slam, steps, paper wrapping etc.) using acoustic data from a multiple microphone set up in the meeting room environment. A system based on hidden Markov models and multichannel audio data was implemented. Mel-Frequency Cepstral Coe cients are used to represent the power spectrum of the acoustic signal. Fully-connected three-state hidden Markov models are trained for 12 acoustic events and one-state models are trained for speech, silence, and unknown events. The system performed adequately compared to other participant in CLEAR2007 evaluation.
منابع مشابه
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task. Real life sound recordings typically have many overlapping sound events, making it hard to recognize with just mono channel audio. Human listeners have been successfully recognizing the mixture of over...
متن کاملA report on sound event detection with different binaural features
In this paper, we compare the performance of using binaural audio features in place of single channel features for sound event detection. Three different binaural features are studied and evaluated on the publicly available TUT Sound Events 2017 dataset of length 70 minutes. Sound event detection is performed separately with single channel and binaural features using stacked convolutional and r...
متن کاملMultichannel Sound Event Detection Using 3D Convolutional Neural Networks for Learning Inter-channel Features
In this paper, we propose a stacked convolutional and recurrent neural network (CRNN) with a 3D convolutional neural network (CNN) in the first layer for the multichannel sound event detection (SED) task. The 3D CNN enables the network to simultaneously learn the interand intra-channel features from the input multichannel audio. In order to evaluate the proposed method, multichannel audio datas...
متن کاملCoupled Sparse Nmf vs. Random Forest Classification for Real Life Acoustic Event Detection
In this paper, we propose two methods for polyphonic Acoustic Event Detection (AED) in real life environments. The first method is based on Coupled Sparse Non-negative Matrix Factorization (CSNMF) of spectral representations and their corresponding class activity annotations. The second method is based on Multi-class Random Forest (MRF) classification of time-frequency patches. We compare the p...
متن کاملFrame-Wise Dynamic Threshold Based Polyphonic Acoustic Event Detection
Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt multi-label classification techniques to perform the polyphonic acoustic event detection with a global threshold to detect the active acoustic events. However, the global threshold has to be set manually and is highly...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007