WSJ0-2mix is a speech recognition corpus of speech mixtures using utterances from the Wall Street Journal (WSJ0) corpus.
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The WSJ0 Hipster Ambient Mixtures (WHAM!) dataset pairs each two-speaker mixture in the wsj0-2mix dataset with a unique noise background scene. It has an extension called WHAMR! that adds artificial reverberation to the speech signals in addition to the background noise.
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Continuous speech separation (CSS) is an approach to handling overlapped speech in conversational audio signals. A real recorded dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate a conversation and capturing the audio replays with far-field microphones.
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LibriMix is an open-source alternative to wsj0-2mix. Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM!.
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WHAMR! is a dataset for noisy and reverberant speech separation. It extends WHAM! by introducing synthetic reverberation to the speech sources in addition to the existing noise. Room impulse responses were generated and convolved using pyroomacoustics. Reverberation times were chosen to approximate domestic and classroom environments (expected to be similar to the restaurants and coffee shops where the WHAM! noise was collected), and further classified as high, medium, and low reverberation based on a qualitative assessment of the mixture’s noise recording.
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The iKala dataset is a singing voice separation dataset that comprises of 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX data mining contest). The music accompaniment and the singing voice are recorded at the left and right channels respectively. Additionally, the human-labeled pitch contours and timestamped lyrics are provided.
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MIR-1K (Multimedia Information Retrieval lab, 1000 song clips) is a dataset designed for singing voice separation. It contains:
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The QMUL underGround Re-IDentification (GRID) dataset contains 250 pedestrian image pairs. Each pair contains two images of the same individual seen from different camera views. All images are captured from 8 disjoint camera views installed in a busy underground station. The figures beside show a snapshot of each of the camera views of the station and sample images in the dataset. The dataset is challenging due to variations of pose, colours, lighting changes; as well as poor image quality caused by low spatial resolution.
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Real-M is a crowd-sourced speech-separation corpus of real-life mixtures. The mixtures are recorded in different acoustic environments using a wide variety of recording devices such as laptops and smartphones, thus reflecting more closely potential application scenarios.
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Description: Male audio data of American English. It is recorded by American English native speakers, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.
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Here we release the dataset (Multi_Channel_Grid, abbreviated as MC_Grid) used in our paper LIMUSE: LIGHTWEIGHT MULTI-MODAL SPEAKER EXTRACTION.
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WHAMR_ext is an extension to the WHAMR corpus with larger RT60 values (between 1s and 3s)
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Chinese wake-up words audio data captured by mobile phone, collected from 200 people, 180 sentences per person, a total length of 24.5 hours; recording staff come from seven dialect regions with balanced gender distribution; collection environment was diversified; recorded text includes wake-up words and colloquial sentences.
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