WAV

To support research on multimodal speech emotion recognition (SER), we developed a dual-channel emotional speech database featuring synchronized recordings of bone-conducted (BC) and air-conducted (AC) speech. The recordings were conducted in a professionally treated anechoic chamber with 100 gender-balanced volunteers. AC speech was captured via a digital microphone on the left channel, while BC speech was recorded from an in-ear BC microphone on the right channel, both at a 44.1 kHz sampling rate to ensure high-fidelity audio. 

Speech recognition in noisy environments has long posed a challenge. Typically used air conduction microphone (ACM) is susceptible to environmental noise. 

QiandaoEar22 is a high-quality noise dataset designed for identifying specific ships among multiple underwater acoustic targets using ship-radiated noise. This dataset includes 9 hours and 28 minutes of real-world ship-radiated noise data and 21 hours and 58 minutes of background noise data.

This is the official Thaat and Raga Forest (TRF) Dataset

Please do cite our paper: Link to Paper

Dataset is also available here: Link to Dataset

Link to our repository: Github Repo

The "Thaat and Raga Forest (TRF) Dataset" represents

AIR-RS-DB: A dataset for classifying Spontaneous and Read Speech

A set of 1028 audio files generated from 7 mp3 files downloaded from All India Radio. https://newsonair.gov.in/ and converted into wav  and then speaker diarized is  using https://huggingface.co/pyannote/speaker-diarization (pyannote/speaker-diarization@2022072,model) and derive 1028 audio files.

These are available as air-rs-db.zip (which can be downloaded)

The dataset consists of three parts, the first part consists of single notes and playing technique samples, and the second includes the triple viewed video, steoro-microphone recordings and 4 track optical vibration recordings in raw file for famous Chinese Folk music ‘Jasmine Flower’ and the first section of ‘Ambush from ten sides’. The third part concerns about the source separated tracks from optical recordings and expressive annotation files are included in the annotation files.

This dataset is generated by GNU Radio.

The steganography and steganalysis of audio, especially compressed audio, have drawn increasing attention in recent years, and various algorithms are proposed. However, there is no standard public dataset for us to verify the efficiency of each proposed algorithm. Therefore, to promote the study field, we construct a dataset including 33038 stereo WAV audio clips with a sampling rate of 44.1 kHz and duration of 10s. And, all audio files are from the Internet through data crawling, which is for a better simulation of a real detection environment.