True's beaked whale.jpg

Western spotted skunk

Hooded skunk

Yellow-throated Marten

Wolverine

True's beaked whale.jpg

Western spotted skunk

Hooded skunk

Yellow-throated Marten

Wolverine

Sound localization

Focus on detecting & localizing human voices.

Human voice
In telephony, the usable voice frequency band ranges from approximately 300 to 3400 Hz. It is for this reason that the ultra low frequency band of the electromagnetic spectrum between 300 and 3000 Hz is also referred to as voice frequency, being the electromagnetic energy that represents acoustic energy at baseband. The bandwidth allocated for a single voice-frequency transmission channel is usually 4 kHz, including guard bands, allowing a sampling rate of 8 kHz to be used.

Projects by other people
Time Difference Of Arrival — Raspberry Pi. Multiple units sync by GPS, determine sound start time.

“Low cost Raspberry Pi sound localizing portable Autonomous Recording Unit (ARU), repo.
Building Sound Source Localization System using Raspberry Pi, repo, paper.
“ODAS localization relies on the Generalized Cross-Correlation with phase Transform method (GCC-PHAT), computed for each pair of microphones. ODAS uses the inverse Fast Fourier Transform (IFFT) to compute the cross-correlation efficiently from the signals in the frequency domain.”
“ODAS supports two sound source separation methods: 1) delay-and-sum beamforming, and 2) geometric sound source separation.”
The ManyEars open framework””Low cost Raspberry Pi sound localizing portable Autonomous Recording Unit (ARU), repo.
Building Sound Source Localization System using Raspberry Pi, repo, paper.
“ODAS localization relies on the Generalized Cross-Correlation with phase Transform method (GCC-PHAT), computed for each pair of microphones. ODAS uses the inverse Fast Fourier Transform (IFFT) to compute the cross-correlation efficiently from the signals in the frequency domain.”
“ODAS supports two sound source separation methods: 1) delay-and-sum beamforming, and 2) geometric sound source separation.”
The ManyEars open framework”, pdf

Arduino Leonardo, 4 mics, code in Localizator_final.ino (sample sound, FFT), link.
Stereo robot ears. Two electret mics, PIC16, link.
Experimenting with Sound Localization and Arduino. LM324/LM326 amps, hardware high pass filter, link.
Lego NXT Spatial Sound Localizing Robot, link, video, v2 link.
Active Bat