Executive Development Programme in Audio AI Fundamentals: Actionable Knowledge
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⢠Introduction to Audio AI Fundamentals: Understanding the basics of Audio AI, its applications, and potential impact on various industries.
⢠Digital Signal Processing (DSP): Exploring the principles of DSP, including filtering, Fourier analysis, and time-frequency analysis.
⢠Neural Networks and Deep Learning: Delving into the architecture of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks.
⢠Audio Feature Extraction: Learning about the various audio features, such as pitch, tempo, and melody, and their importance in audio AI.
⢠Speech Recognition: Understanding the fundamentals of speech recognition, including acoustic modeling, language modeling, and decoding.
⢠Music Information Retrieval (MIR): Exploring the principles of MIR, such as beat tracking, chord estimation, and genre classification.
⢠Natural Language Processing (NLP): Learning about the fundamentals of NLP, including text processing, sentiment analysis, and machine translation.
⢠Ethics in Audio AI: Understanding the ethical considerations in the development and deployment of audio AI, including privacy, fairness, and transparency.
⢠Best Practices in Audio AI Development: Delving into the best practices in developing and deploying audio AI, including data collection, model training, and testing.
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