In this paper, we present a novel approach to relax the constraint of stereo-data which is needed in a series of algorithms for noise-robust speech recognition. As a demonstration...
Nonnegative matrix factorization (NMF) is a widely-used tool for obtaining low-rank approximations of nonnegative data such as digital images, audio signals, textual data, financ...
We present a technique for following a live performance in the situation where a score is not available. Making use of a local alignment between recent and longer term musical inf...
In this paper, independent component analysis (ICA) in a subband domain has been extended into a feed-forward network. The feed-forward network maximizes mutual independence of se...
Annotation of large multilingual corpora remains a challenge to the data-driven approach to speech research, especially for under-resourced languages. This paper presents crosslan...
In this paper, we use information retrieval (IR) techniques to improve a speech recognition (ASR) system. The potential benefits include improved speed, accuracy, and scalability...
The major contribution of this paper is the presentation of a general unifying description of distributed algorithms allowing to map local, node-based, algorithms onto a single gl...
We propose an ℓ1 criterion for dictionary learning for sparse signal representation. Instead of directly searching for the dictionary vectors, our dictionary learning approach i...
Detecting the time of occurrence of an acoustic event (for instance, a cheer) embedded in a longer soundtrack is useful and important for applications such as search and retrieval...
Keansub Lee, Daniel P. W. Ellis, Alexander C. Loui
We propose a novel algorithm for sparse system identification in the frequency domain. Key to our result is the observation that the Fourier transform of the sparse impulse respo...