Manage Data for Audio Processing, Enhancement, & Sound Recognition

Drum up your audio machine learning models across audio processing use cases, for audio & text data

With the rise of audio in the AI space, extraction, analysis, and usage of a tremendous amount of hidden information became possible with the rise of deep learning. Analyzing sentiment and insights concealed in soundwaves, background sounds, and music, helps develop better audio intelligence systems. Additionally, generating novel sounds, music, or speech from text data became possible.

In the speech space, data scientists tackle tasks like text to speech synthesis, speech separation, dialect recognition, speaker recognition, automatic speech recognition, or enhancement. Solving these tasks helps create better voice assistant AI systems, sales intelligence, or surveillance solutions. Next, sound is processed to address sound recognition, sound event detection, and environmental sound classification. The latter helps solve tasks such as enhancing audio via background noise removal/noise cancelling or echo removal or correctly flagging breaking glass to alert homeowners, and the baby cries to alert parents. In their turn, advances in the music AI domain made music enhancement, music source separation, or information retrieval possible.

With Activeloop, machine learning teams working on audio solutions can ingest raw audio data with its metadata to create multimodal audio & text datasets streamable with one line of code. In addition, you can visualize spectrograms, playing select audio slices. Teams can also collaborate on curating their datasets by instantly fetching subsets of interest with our powerful query engine. Lastly, data scientists can stream their materialized audio data while training models in PyTorch or TensorFlow, regardless of scale.