Deep Learning for Audio (DLA)

• Lecture and seminar materials for each week are in ./week* folders, see README.md for materials and instructions
• Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
• The current version of the course is conducted in autumn 2026 at the CS Faculty of HSE.

For previous years versions, see Past Versions section.

Syllabus

• week01 Introduction to Course

  • Lecture: Introduction to Course + Inspiration
  • Seminar: Free talk
  • Self-Study: Introduction to PyTorch and basic devOps

• week02 Introduction to Digital Signal Processing

  • Lecture: Signals, Fourier Transform, spectrograms, MelScale, MFCC
  • Seminar: DSP in practice, spectrogram creation, IRF, frequency filtering

• week03 Automatic Speech Recognition I

  • Lecture: Metrics, Datasets, Connectionist Temporal Classification (CTC), DeepSpeech2, Conformer, Beam Search, Language models
  • Seminar: Audio Augmentations, WER and CER, CTC Decoding

• week04 Automatic Speech Recognition II

  • Lecture: LAS, Hybrid CTC/Attention, OpenAI Whisper, RNN-T, Streaming ASR, Decoder-only ASR
  • Seminar: Whisper: greedy decoding, prompting, alignment in cross-attention, language forcing

TBA

Homeworks and Projects

TBA

See our project template.

Resources

• Lecture recordings on YouTube (in russian)

Some of the weeks have English recordings. See the corresponding sub-directories.

Contributors & course staff

Course materials and teaching (in different years) were delivered by:

• Maxim Kaledin
• Georgy Gospodinov
• Georgiy Pistsov
• Aibek Alanov (previously)
• Petr Grinberg (previously)
• Grigory Fedorov(previously)
• Assel Yermekova (previously)
• Alexander Markovich (previously)
• Daniil Ivanov (previously)
• Ilya Lewin (previously)
• Timofey Smirnov (previously)
• Alexander Mamaev (previously)

Past Versions

• 2025
• 2024
• 2023
• 2022
• 2021
• 2020