Skip to content

References: Signal Processing Applications and AI Integration

  1. Digital signal processor - Wikipedia - Surveys specialized DSP architectures, multiply-accumulate hardware, memory organization, real-time constraints, programming, and embedded applications.

  2. Field-programmable gate array - Wikipedia - Explains configurable logic, HDL design, parallelism, DSP blocks, development flow, performance, and application tradeoffs.

  3. Deep learning - Wikipedia - Introduces neural architectures, representation learning, training, applications, limitations, and relationships to signal, image, speech, and video processing.

  4. Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville - MIT Press - The authors’ layered representation viewpoint became the standard bridge from linear signal transformations to learned features.

  5. Real-Time Digital Signal Processing: Implementations and Applications (3rd Edition) - Sen M. Kuo, Bob H. Lee, and Wenshun Tian - Wiley - The authors’ laboratory-driven workflow connects algorithms to fixed-point processors, optimization, interfaces, and real-time constraints.

  6. Simple Audio Recognition - TensorFlow - Demonstrates waveform preprocessing, STFT spectrogram generation, model training, evaluation, and deployment-oriented audio classification.

  7. Audio Data Preparation and Augmentation - TensorFlow I/O - Covers audio loading, resampling, trimming, fading, spectrograms, masks, and data augmentation for machine-learning pipelines.

  8. Introduction to Audio - Fastaudio - Explains waveforms, sample rates, spectrograms, mel scales, and practical feature choices for audio learning systems.

  9. DSP 101 Part 1 - Analog Devices - Connects signal acquisition, conversion, filtering, processor selection, and implementation constraints in practical DSP systems.

  10. SciPy Signal Processing - SciPy - Provides production-quality filtering, transforms, spectral analysis, resampling, and system tools useful for AI preprocessing pipelines.