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  • Sampo Vesa
  • IEEE Transactions on Audio, Speech, and Language…
  • 2009
A method for learning the distance of a sound source in a room is presented. The proposed method is based on short-time magnitude-squared coherence between the two channels of a binaural signal. Based on white noise as the training data, a coherence profile is obtained at each desired position in the room. These profiles can then be used to identify the(More)
The concept of augmented reality audio characterizes techniques where real sound environment is extended with virtual auditory environments and communications scenarios. This article introduces a framework for Wearable Augmented Reality Audio (WARA) based on a speci c headset con guration and a real-time audio software system. We will review relevant(More)
  • Sampo Vesa
  • 2007 IEEE Workshop on Applications of Signal…
  • 2007
A learning approach for estimating sound source distance based on binaural signals is presented. The frequency-dependent coherence between the left and right ear signals is used as the distance cue. The distance estimation is based on pre-calculated coherence profiles and an energy-weighted likelihood function. The system is evaluated with different speech(More)
In this paper, a novel method for analysis of binaural room impulse responses is presented. Individual reflections are localized in time and frequency from a measured binaural room impulse response based on the continuous cross-wavelet transform (XWT). The directions of the reflections are then analyzed based on KEMAR and CIPIC reference data lookup, and(More)
The effect of the choice of features on unsupervised clustering in audio surveillance is investigated. The importance of individual features in a larger feature set is first analyzed by examining the component loadings in principal component analysis (PCA). The individual sound events are then assigned into clusters using the self-tuning spectral clustering(More)
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