Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Mandel, Michael I"'
Autor:
Sivakumar, Viswanath, Seely, Jeffrey, Du, Alan, Bittner, Sean R, Berenzweig, Adam, Bolarinwa, Anuoluwapo, Gramfort, Alexandre, Mandel, Michael I
Surface electromyography (sEMG) non-invasively measures signals generated by muscle activity with sufficient sensitivity to detect individual spinal neurons and richness to identify dozens of gestures and their nuances. Wearable wrist-based sEMG sens
Externí odkaz:
http://arxiv.org/abs/2410.20081
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, notably in connecting ideas and adhering to logical rules to solve problems. These models have evolved to accommodate various data modalities, including sound and image
Externí odkaz:
http://arxiv.org/abs/2406.04615
Across various research domains, remotely-sensed weather products are valuable for answering many scientific questions; however, their temporal and spatial resolutions are often too coarse to answer many questions. For instance, in wildlife research,
Externí odkaz:
http://arxiv.org/abs/2309.16867
We introduce ImportantAug, a technique to augment training data for speech classification and recognition models by adding noise to unimportant regions of the speech and not to important regions. Importance is predicted for each utterance by a data a
Externí odkaz:
http://arxiv.org/abs/2112.07156
Spatial clustering techniques can achieve significant multi-channel noise reduction across relatively arbitrary microphone configurations, but have difficulty incorporating a detailed speech/noise model. In contrast, LSTM neural networks have success
Externí odkaz:
http://arxiv.org/abs/2012.02191
This paper aims at eliminating the interfering speakers' speech, additive noise, and reverberation from the noisy multi-talker speech mixture that benefits automatic speech recognition (ASR) backend. While the recently proposed Weighted Power minimiz
Externí odkaz:
http://arxiv.org/abs/2011.09162
Autor:
Trinh, Viet Anh, Mandel, Michael I
Publikováno v:
Proceedings of Interspeech 2020
In this paper, we propose a metric that we call the structured saliency benchmark (SSBM) to evaluate importance maps computed for automatic speech recognizers on individual utterances. These maps indicate time-frequency points of the utterance that a
Externí odkaz:
http://arxiv.org/abs/2005.10929
Autor:
Maiti, Soumi, Mandel, Michael I
Traditional speech enhancement systems produce speech with compromised quality. Here we propose to use the high quality speech generation capability of neural vocoders for better quality speech enhancement. We term this parametric resynthesis (PR). I
Externí odkaz:
http://arxiv.org/abs/1911.06266
Autor:
Ni, Zhaoheng, Mandel, Michael I
Speaker separation refers to isolating speech of interest in a multi-talker environment. Most methods apply real-valued Time-Frequency (T-F) masks to the mixture Short-Time Fourier Transform (STFT) to reconstruct the clean speech. Hence there is an u
Externí odkaz:
http://arxiv.org/abs/1911.02746
Autor:
Ni, Zhaoheng, Mandel, Michael I
Speech separation is an essential task for multi-talker speech recognition. Recently many deep learning approaches are proposed and have been constantly refreshing the state-of-the-art performances. The lack of algorithm implementations limits resear
Externí odkaz:
http://arxiv.org/abs/1911.00982