Dataset Overview

Name

Sony‑TAu Realistic Spatial Soundscapes 2022 (STARSS22)

Description

Overview

• Content: Multichannel sound‑scene recordings with temporal and spatial annotations of salient events.
• Collection Sites: University of Tampere (TAU), Finland, and Sony, Japan.
• Recording Formats: Two 4‑channel spatial formats – microphone array (MIC) and first‑order Ambisonics (FOA).
• Purpose: Development dataset for DCASE 2022 sound event localization and detection tasks.

Recording Details

• Period: September 2021 – February 2022.
• Equipment: Eigenmike em32 microphone array and Ricoh Theta V 360° video recorder.
• Tracking System: Optitrack Flex 13 optical tracker.
• Duration: ~2 h from 70 SONY clips and ~3 h from 51 TAU clips.

Specifications

• Number of Recordings: 111 clips.
• Number of Rooms: 11 distinct rooms.
• Sample Rate: 24 kHz.
• Formats: Two 4‑channel 3‑D recording formats.
• Target Event Classes: 13 categories.

Event Classes

• Count: 13 categories (e.g., female speech, male speech, applause, telephone ring, laughter, etc.).

Naming Convention

• File name pattern: fold[fold number]_room[room number]_mix[recording number per room].wav

Task Setup

• Train‑Test Split: Predefined splits provided.
• Evaluation: Models should be trained on the training split and reported on the test split.

Directory Structure

• Root: Contains README.md, LICENSE.
• Subfolders: foa_dev (Ambisonic) and mic_dev (microphone array) holding the respective recordings.

Usage

• Applicable Tasks: Sound Event Detection (SED), Sound Source Localization, Sound Event Localization and Detection (SELD).
• Guidelines: Follow the DCASE 2022 challenge instructions, using the provided train‑test splits for model development and assessment.