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Determination of pitch in noise is challenging because of corrupted harmonic structure. In this paper, we extract pitch using supervised learning, where probabilistic pitch states are directly learned from noisy speech. We investigate two alternative neural networks modeling the pitch states given observations. The first one is the feedforward deep neural network (DNN), which is trained on static...
In this paper we propose the use of Long Short-Term Memory recurrent neural networks for speech enhancement. Networks are trained to predict clean speech as well as noise features from noisy speech features, and a magnitude domain soft mask is constructed from these features. Extensive tests are run on 73 k noisy and reverberated utterances from the Audio-Visual Interest Corpus of spontaneous, emotionally...
Recurrent neural networks (RNNs) have recently produced record setting performance in language modeling and word-labeling tasks. In the word-labeling task, the RNN is used analogously to the more traditional conditional random field (CRF) to assign a label to each word in an input sequence, and has been shown to significantly outperform CRFs. In contrast to CRFs, RNNs operate in an online fashion...
This paper describes our joint efforts to provide robust automatic speech recognition (ASR) for reverberated environments, such as in hands-free human-machine interaction. We investigate blind feature space de-reverberation and deep recurrent de-noising auto-encoders (DAE) in an early fusion scheme. Results on the 2014 REVERB Challenge development set indicate that the DAE front-end provides complementary...
Non-verbal speech cues play an important role in human communication such as expressing emotional states or maintaining the conversational flow. In this paper we investigate the effect of applying deep bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks to the Interspeech 2013 Computational Paralinguistics Social Signals Sub-Challenge dataset requiring frame-wise, speaker-independent...
This paper proposes a novel machine learning approach for the task of on-line continuous-time music mood regression, i.e., low-latency prediction of the time-varying arousal and valence in musical pieces. On the front-end, a large set of segmental acoustic features is extracted to model short-term variations. Then, multi-variate regression is performed by deep recurrent neural networks to model longer-range...
This paper seeks to exploit high-level temporal information during feature extraction from audio signals via non-negative matrix factorization. Contrary to existing approaches that impose local temporal constraints, we train powerful recurrent neural network models to capture long-term temporal dependencies and event co-occurrence in the data. This gives our method the ability to “fill in the blanks”...
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