Pitch Estimation Dataset Released for Fundamental Frequency Analysis
A comprehensive dataset of speech and noise corpora has been published for evaluating fundamental frequency estimation algorithms. The dataset, released on June 29, 2020, includes eight corpora with varying licenses, allowing for redistribution and use in research and development.
Key points
- The dataset, titled 'Speech and Noise Corpora for Pitch Estimation of Human Speech', was published by Jade Hochschule on zenodo.org.
- The dataset includes eight corpora: CMU-ARCTIC, FDA, KEELE, MOCHA-TIMIT, PTDB-TUG, NOISEX, QUT-NOISE, and CMU-ARCTIC.
- Each corpus has a different license, ranging from BSD to CC-BY-SA, allowing for varying levels of redistribution and use.
- The dataset is intended for use in evaluating fundamental frequency estimation algorithms and is available for free download.
A comprehensive dataset of speech and noise corpora has been published for evaluating fundamental frequency estimation algorithms. The dataset, titled 'Speech and Noise Corpora for Pitch Estimation of Human Speech', was published by Jade Hochschule on zenodo.org on June 29, 2020.
The dataset includes eight corpora, each with its own unique characteristics and licenses. The CMU-ARCTIC corpus, for example, is available under the BSD license, while the QUT-NOISE corpus is licensed under CC-BY-SA.
The dataset is intended for use in evaluating fundamental frequency estimation algorithms, and is available for free download. Researchers and developers can use the dataset to test and evaluate their algorithms, and to improve the accuracy of pitch estimation.
The release of this dataset is significant because it provides a standardized set of data for evaluating pitch estimation algorithms. This can help to improve the accuracy of speech recognition systems, and to enable more effective use of speech in various applications.
The dataset is also notable for its comprehensive coverage of speech and noise corpora. The inclusion of eight corpora provides a wide range of data for researchers and developers to work with, and can help to improve the robustness of pitch estimation algorithms.
Overall, the release of this dataset is an important step forward in the development of pitch estimation algorithms, and can help to improve the accuracy of speech recognition systems.
Sources
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