Manage Data for Audio Processing, Enhancement, & Sound Recognition
Build better solutions for noise cancelling, sound recognition, audio enhancement, automatic speech recognition, & more
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Machine Learning for Denoising, Enhancing Audio, Recognizing Sounds, Speech, & Processing Audio Like a Pro
Shipping AI products feels like a jam session with Database for AI used for audio processing. Work on multimodal text & audio datasets. Never skip a beat with your ML models for noise cancellation in audio devices or virtual meetings, sound & speech recognition for digital assistants, surveillance systems, as well as generating new music or human-like speech
Noise cancelling
Train ML models to remove background noise or echo from audio, leaving only the voices & sounds your users want to hear
Voice & music generation
Build AI apps to generate human voices, power text to speech solutions, or create original music scores
Audio Enhancement
Embed audio enhancement models for a crispier, cleaner, & more consistent sound, as well as tonally correct recordings
Automatic Speech Recognition
Use the composition of audio and voice signals to process speech and power voice assistants, as well as automated telephony systems
Text to speech generation
Turn written words into “phonemic representations”, convert the latter into waveforms, & output as human speech - for content, voice assistants, & more
Sound recognition
Deploy machine learning models to recognize human speech, natural sounds, & music. Develop solutions for disability assistance and surveillance systems
Audio Machine Learning Datasets for Speech Synthesis, Speech Recognition, Sound Recognition, & Audio Enhancement
Don't have proprietary data? Get a head start with one of the public machine learning datasets for audio processing available via Activeloop for text to speech generation, automatic speech recognition, background noise removal, sound recognition, & more
- Explore multimodal
audio & text datasets ...
- ... to detect speech, multiple
speakers, or to develop noise
cancelling solutions ...
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- ... or build text-to-speech apps!
Break the sound barrier for model deployment with Audio ML data infrastructure from Activeloop
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.
Case Study: Sound-Based Infant Medical Diagnostics
Baby’s cry can tell a lot about infant’s health. Despite Ubenwa’s unmatched success in baby diagnostics, the lack of scalable & streamlined audio data infrastructure had them longing for a lullaby. Discover how we turned their data pipelines into a rhythmic giggle of efficiency
Radically Better Audio ML Infrastructure at Ubenwa
Ubenwa, an AI-powered infant cry diagnostics company, faced data standardization, audio support, & scalability challenges. Learn how by streamlining their data pipeline, they doubled efficiency and enhanced machine learning models for neonatal distress detection.
