Internship on Audio Processing and Machine Learning

Key Skills

Data Analysis, Data cleaning / Data cleansing, Design, Keras software library, Machine learning techniques, Python Programming, PyTorch, TensorFlow

Job Description

The Music Technology team at Sony Research is looking for Research Interns who are passionate about machine learning for audio signal processing.

Our mission is to research and develop technologies for various Sony Group products and for scientific publication. The technologies developed in the internship will have the potential to be applied in music and film production tasks in entertainment studios around the world.

The workplace language is English, and our team is composed of members from a variety of countries.

Responsibilities

As a Research Intern, you will investigate and apply novel algorithms that combine audio signal processing and machine learning techniques in areas such as music generation, music inpainting, source separation, sound synthesis, signal restoration, automatic sonification, and post-production of music or movies.

You are expected to implement and optimize these algorithms by applying your research, coding, and problem-solving skills. You will be supported in your efforts by having access to state-of-the-art resources at Sony and a unique and extensive catalog of music data.

Based in Tokyo, you will also have the opportunity to collaborate with other Sony research teams and Sony Business Groups such as Sony Music, Sony Pictures, and Sony Interactive Entertainment.

Required Qualifications

• Master`s degree in CS, EE, applied mathematics, or a related field.
• Proven knowledge of machine learning.
• Expertise in audio signal analysis, processing, and feature extraction.
• Experience cleaning, manipulating, and analyzing audio data.
• Proven knowledge of deep learning architecture design and training/testing methodologies.
• Proven coding expertise with Python.
• Experience with machine learning tools (PyTorch, TensorFlow, Keras).

Preferred qualifications

• Enrolled in a relevant Ph.D. program.
• Record of relevant scientific publications.
• Interest in music/audio.