In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate this summarization’s performance, we adopt an extrinsic approach: we compare a Fado Genre Classifier’s performance using truncated contiguous clips against the summaries extracted with those algorithms on 2 different datasets. We show that Maximal Marginal Relevance (MMR), LexRank and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing.

Answer: On the Application of Generic Summarization Algorithms to Music


Question: This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis. The technique allows us to automatically extract low-dimensional features from high dimensional spectral features in a non-linear, data-driven, unsupervised way. We compared the new stochastic feature extractor with conventional mel-cepstral analysis in analysis-by-synthesis and text-to-speech experiments. Our results confirm that the proposed method increases the quality of synthetic speech in both experiments.

Answer: DEEP DENOISING AUTO-ENCODER FOR STATISTICAL SPEECH SYNTHESIS


Question: The sample complexity of active learning under the realizability assumption has been well-studied. The realizability assumption, however, rarely holds in practice. In this paper, we theoretically characterize the sample complexity of active learning in the non-realizable case under multiview setting. We prove that, with unbounded Tsybakov noise, the sample complexity of multiview active learning can be Õ(log 1ǫ ), contrasting to single-view setting where the polynomial improvement is the best possible achievement. We also prove that in general multi-view setting the sample complexity of active learning with unbounded Tsybakov noise is Õ(1ǫ ), where the order of 1/ǫ is independent of the parameter in Tsybakov noise, contrasting to previous polynomial bounds where the order of 1/ǫ is related to the parameter in Tsybakov noise.

Answer:
Multi-View Active Learning in the Non-Realizable Case