Machine Learning for Audio Version 12 audio processing and analysis provides high-level built-in functions for audio identification, speech recognition and more. An efficient and tight integration with the machine learning and neural net framework, as well as easy access to a growing number of state-of-the-art pre-trained models available through the Wolfram Neural Net Repository enables easy prototyping and development of algorithms. All of these capabilities form a rich, productive system to apply high-level and accurate machine learning solutions to a wide range of fields, such as speech and music. Built-in speech recognition. » Built-in pitch recognition. » Built-in audio identification. » Advanced audio processing & analysis capabilities. » Comprehensive audio support in machine learning functions. » Integrated feature extraction and feature space plot. » Immediately accessible trained and untrained neural nets. » Efficient new audio net encoders. » Support for recurrent networks. » Support for GPU training and evaluation. » Related Examples Inspect Speech Using a Neural Net » Recognize Keywords in a Speech » Build an Audio-Enabled Question-Answering System » Recognize Pitch » Identify Sounds » Identify Animal Sounds » Inspect a Signal Using the Audio Identification Net » Classify Instruments Using Audio Identify Features » Automatically Extract Audio Features » Extract a Specific Feature » Extract Features Using a Neural Net » Cluster Sounds Using Audio Features »
