Audio
The LibriSpeech corpus, a publicly available English speech dataset derived from audiobook recordings. The corpus contains approximately 1,000 hours of 16 kHz read speech from over 2,400 speakers, encompassing diverse speaking styles, rates, and regional accents. For the purpose of contrastive learning, a subset of 100 speakers was sampled, with 20 utterances per speaker ranging from 3 to 10 seconds. The dataset provides clean, labeled speech suitable for tasks involving speaker representation, acoustic modeling, and multi-style synthesis.
- Categories:
11 Views
This dataset contains a diverse range of file types, including text, images, and audio, designed for multi-modal analysis and research. It includes text files (txt) with both structured and unstructured data, suitable for natural language processing tasks such as sentiment analysis and text classification. The image files cover various subjects and are intended for computer vision tasks like object detection and classification. Additionally, the dataset includes audio files in formats like MP3 and WAV, supporting speech recognition and sound analysis.
- Categories:
44 Views
This dataset consists of carefully curated audio recordings that capture the distinct sounds produced by multiple individuals walking in various environments. Designed to support research in sound recognition, activity analysis, and the study of human behaviour, it provides a rich resource for understanding how group dynamics influence acoustic patterns. Each recording is accompanied by detailed metadata, including the number of participants, environmental context, and surface characteristics.
- Categories:
271 Views
The large and diverse access to data sources in healthcare has boosted the application of novel computer techniques that can extract meaningful information to improve patients' prognoses and other important medical uses. However, current systems require the professional to manually type the information, which increases the risk of transcription errors and cross-contamination. We propose an automated system that allows healthcare professionals to dictate clinical information that can be transcribed and analyzed.
- Categories:
128 Views
Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas.
- Categories:
2166 Views
The sound part is built into many products.
It is used not only in audio systems, but also in a wide range of industries such as home theaters, broadcast amplifier systems, TVs, computers, AI speakers, and game consoles.
Even now, many companies are making efforts to improve the sound quality of the acoustic part.
In the future, high sound quality will be required in many industrial fields.
A wide range of industries will require high-quality technology.
- Categories:
120 Views
We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c
- Categories:
1620 Views
Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists. This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.
- Categories:
683 Views
The Heidelberg Spiking Datasets comprise two spike-based classification datasets: The Spiking Heidelberg Digits (SHD) dataset and the Spiking Speech Command (SSC) dataset. The latter is derived from Pete Warden's Speech Commands dataset (https://arxiv.org/abs/1804.03209), whereas the former is based on a spoken digit dataset recorded in-house and included in this repository. Both datasets were generated by applying a detailed inner ear model to audio recordings. We distribute the input spikes and target labels in HDF5 format.
- Categories:
2807 Views
The dataset consists of EEG recordings obtained when subjects are listening to different utterances : a, i, u, bed, please, sad. A limited number of EEG recordings where also obtained when the three vowels were corrupted by white and babble noise at an SNR of 0dB. Recordings were performed on 8 healthy subjects.
- Categories:
1503 Views