Intelligent sound recognition using spiking neural network
Project outside the university
InnateraInnatera is a semiconductor company that develops microprocessors that are based on the architecture of the brain.
These devices mimic the brain’s mechanisms for processing fast information streams from sensors, and enable
complex, turn-key sensor analytics functionalities, with 10,000x higher performance per watt than conventional
microprocessors. Innatera’s technology enables intelligence functionalities to be realized in devices at the extreme
edge, and is a critical enabler for next-generation applications in the IoT, wearable, embedded, and automotive
domains. Innatera is a spin-off of the Delft University of Technology, and is based on the university campus in Delft.
Project description
The ability to recognize sound is an important requirement in modern electronic devices, especially since the
introduction of intelligent assistants like the Amazon Alexa. However, this trend is also observed in other markets
including industrial electronics, security, and automotive. Common use-cases built on sound recognition include:
- Voice and spoken word recognition
- Aggression/accident detection in crowded spaces
- Speaker identification
- Sound localization
- Anomaly detection in machines
A large majority of such use-cases are passive in nature, i.e. involve continuous monitoring of the environment until
the relevant event occurs. This means that the listening functions are continuously active (always-on), and the
processing pipeline that interprets the resulting data stream is continuously running. When such use-cases are
realized in power-limited devices (eg. wearables with a limited battery capacity, or industrial system within a small,
thermally constrained housing), power dissipation of the sensing and processing functions becomes a critical factor.
Notably, the viability of intelligent sensing concepts is predicated upon the power dissipation of the processing
pipeline being maintained within a narrow envelope, typically under 10mW for wearables. Conventional signal
processing approaches applied to these use-cases generally incur a power cost that lies outside of this envelope.
Objective
In this thesis assignment, you will explore brain-inspired algorithms for carrying out sound recognition in cuttingedge,
power-constrained application use-cases. The brain relies on a highly-efficient neural network – the spiking
neural network – to process sensor information from the sense organs. In this assignment, you will explore and
develop spiking neural network concepts for sound processing, and develop an algorithm that enables:
- Characterization of sounds in audio data (eg. from a microphone)
- Identification of characterized sounds
- Mitigation of noise and other undesirable characteristics
As part of the assignment, you will explore the design space for the problem, evaluate and benchmark state-of-theart
solutions, develop an algorithm using a high-level simulation framework, and subsequently, potentially implement
the developed algorithm on Innatera’s experimental hardware platform.
This assignment is an exciting opportunity to participate in a highly innovative technology development, and offers
a chance to shape how Innatera’s ground-breaking processors will address the needs of next-generation use-cases
in the industry.
Location & Supervision
This assignment will be carried out at Innatera Nanosystems in Delft, and will be performed under the supervision
of dr. Richard Hendriks of the Circuits & Systems Group, TUDelft.
Contact
dr.ir. Richard Hendriks
Signal Processing Systems Group
Department of Microelectronics
Last modified: 2020-12-01