Audio Classification Use Cases in Wasabi AiR

Audio classification automatically identifies and categorizes sounds or audio clips based on predefined labels such as speech, music, or environmental noise. Use cases include:

Use Case	Description	Benefit
Speech versus Non-Speech Detection	Distinguish between spoken content, music, and background noise.	Provides a useful tool for call centers, podcast editing, or automatic transcription pipelines, increasing efficiency.
Music Genre and Mood Classification	Classify songs by genre, mood, or tempo.	Powers music recommendation engines and playlist curation.
Environmental Sound Recognition	Identify sounds such as sirens, alarms, traffic, or nature.	Provides a useful tool for smart cities, surveillance, and safety monitoring, providing additional context.
Sound Event Detection in Media	Detect specific audio events in videos, such as applause, laughter, gunshots, or explosions.	Supports automated highlight tagging, content moderation, and indexing.
Customer Call Categorization	Classify calls by topic (complaint, inquiry, support type) using audio features.	Enhances the quality of CRM data and workflow automation.
Animal and Wildlife Monitoring	Identify species by their vocalizations for ecological research or conservation.	Automates data collection in the field.
Anomaly Detection in Industrial Sounds	Detect irregular machine noises or equipment failures.	Provides a useful tool for enhancing predictive maintenance in manufacturing and transportation.
Security and Surveillance Audio Alerts	Detect gunshots, glass breaking, or aggressive shouts in real time.	Enables rapid response for public safety or security operations.
Sports Event Sound Analysis	Identify whistles, buzzer sounds, crowd reactions, or referee signals.	Automates tagging of highlights or in-game analytics.
Podcast and Media Content Indexing	Automatically classify segments (interview, monologue, advertisement, music) for search and analytics.	Supports content recommendation and ad targeting.