Few-Shot Audio Classification

VoxCeleb1 is an audio dataset containing over 100,000 utterances for 1,251 celebrities, extracted from videos uploaded to YouTube.

Common Voice is an audio dataset that consists of a unique MP3 and corresponding text file. There are 9,283 recorded hours in the dataset. The dataset also includes demographic metadata like age, sex, and accent. The dataset consists of 7,335 validated hours in 60 languages.

The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification. It comprises 2000 5s-clips of 50 different classes across natural, human and domestic sounds, again, drawn from Freesound.org. Source: The NIGENS General Sound Events Database Image Source: https://github.com/karolpiczak/ESC-50

NSynth is a dataset of one shot instrumental notes, containing 305,979 musical notes with unique pitch, timbre and envelope. The sounds were collected from 1006 instruments from commercial sample libraries and are annotated based on their source (acoustic, electronic or synthetic), instrument family and sonic qualities. The instrument families used in the annotation are bass, brass, flute, guitar, keyboard, mallet, organ, reed, string, synth lead and vocal. Four second monophonic 16kHz audio snippets were generated (notes) for the instruments. Source: Data Augmentation for Instrument Classification Robust to Audio Effects Image Source: https://magenta.tensorflow.org/nsynth

CREMA-D is an emotional multimodal actor data set of 7,442 original clips from 91 actors. These clips were from 48 male and 43 female actors between the ages of 20 and 74 coming from a variety of races and ethnicities (African America, Asian, Caucasian, Hispanic, and Unspecified). Actors spoke from a selection of 12 sentences. The sentences were presented using one of six different emotions (Anger, Disgust, Fear, Happy, Neutral, and Sad) and four different emotion levels (Low, Medium, High, and Unspecified). Participants rated the emotion and emotion levels based on the combined audiovisual presentation, the video alone, and the audio alone. Due to the large number of ratings needed, this effort was crowd-sourced and a total of 2443 participants each rated 90 unique clips, 30 audio, 30 visual, and 30 audio-visual. 95% of the clips have more than 7 ratings.

FSDKaggle2018 is an audio dataset containing 11,073 audio files annotated with 41 labels of the AudioSet Ontology. FSDKaggle2018 has been used for the DCASE Challenge 2018 Task 2. All audio samples are gathered from Freesound and are provided as uncompressed PCM 16 bit, 44.1 kHz mono audio files. The 41 categories of the AudioSet Ontology are: "Acoustic_guitar", "Applause", "Bark", "Bass_drum", "Burping_or_eructation", "Bus", "Cello", "Chime", "Clarinet", "Computer_keyboard", "Cough", "Cowbell", "Double_bass", "Drawer_open_or_close", "Electric_piano", "Fart", "Finger_snapping", "Fireworks", "Flute", "Glockenspiel", "Gong", "Gunshot_or_gunfire", "Harmonica", "Hi-hat", "Keys_jangling", "Knock", "Laughter", "Meow", "Microwave_oven", "Oboe", "Saxophone", "Scissors", "Shatter", "Snare_drum", "Squeak", "Tambourine", "Tearing", "Telephone", "Trumpet", "Violin_or_fiddle", "Writing". Source: https://zenodo.org/record/2552860 Image Source: https://labs.freesound.org/datasets/

Due to the highly variable sample size of the original BirdClef2020 dataset and the issues that it presents with reproducibility, we propose a pruned version of the set, where samples longer than 180s are removed along with classes with fewer than 50 samples. This processing puts it further in line with other complex audio datasets and allows for experimentation on more consumer friendly hardware.

The speech accent archive uniformly presents a large set of speech samples from a variety of language backgrounds. Native and non-native speakers of English read the same paragraph and are carefully transcribed. The archive is used by people who wish to compare and analyze the accents of different English speakers.

One of the founding fathers of marine mammal bioacoustics, William Watkins, carried out pioneering work with William Schevill at the Woods Hole Oceanographic Institution for more than four decades, laying the groundwork for our field today. One of the lasting achievements of his career was the Watkins Marine Mammal Sound Database, a resource that contains approximately 2000 unique recordings of more than 60 species of marine mammals (Table 1). Recordings were made by Watkins and Schevill as well as many others, including G. C. Ray, D. Wartzok, D. and M. Caldwell, K. Norris, and T. Poulter. Most of these have been digitized, along with approximately 15,000 annotated digital sound clips. The Watkins database has enormous historical and scientific value. The recordings provide sounds professionally identified as produced by particular marine mammal species in defined geographic regions (Figure 1) during specific seasons, which can be used as reference datasets for marine mammal detections from the growing amounts of passive acoustic monitoring (PAM) data that are being collected worldwide. In addition, the archive contains recordings that span seven decades, from the 1940's to the 2000's, and includes the very first recordings of 51 species of marine mammals. These data provide a rich resource to efforts aimed at examining long-term changes in vocal production that may be related to changes in ambient noise levels, as well as serve as a voucher collection for many species. We have made this resource fully accessible online, as was Watkins' goal (see crediting information below). The final product enables investigators, educators, students, and the public worldwide to freely and easily access acoustic samples from identified species of marine mammals, and place these samples in a geographic and temporal context. The physical collection has been donated to the New Bedford Whaling Museum.