AI Audio Classifier
Identify and classify sounds using AI that runs 100% in your browser. Upload an audio file or record via your microphone to detect speech, music, dog barking, car horns, and 500+ sound types. Your audio files never leave your browser — completely private.
Drop an audio file here or click to upload
Supports WAV, MP3, OGG, WebM, FLAC — max 25MB
Click to start recording from your microphone
Classification Results
Top 5 Classifications
How AI Audio Classification Works in Your Browser
This tool uses the Audio Spectrogram Transformer (AST) model, fine-tuned on the AudioSet dataset containing over 500 sound categories. Running directly in your browser via Transformers.js, it converts your audio into a mel spectrogram (a visual representation of frequencies over time) and then classifies what type of sound it is. The entire process happens locally — your audio files are never uploaded to any server.
The AudioSet dataset includes categories ranging from everyday sounds (dog barking, car horn, doorbell) to musical instruments, human activities, nature sounds, and environmental noise. The model can identify subtle differences between similar sounds, such as distinguishing between a piano and a guitar, or between rain and a waterfall.
Types of Sounds Detected
- Human sounds — Speech, singing, laughter, coughing, clapping, whistling
- Animal sounds — Dog barking, cat meowing, bird chirping, rooster crowing
- Music — Piano, guitar, drums, violin, electronic music, and many more instruments
- Environmental — Rain, thunder, wind, ocean waves, fire crackling
- Vehicles — Car horn, engine, siren, train, airplane
- Household — Doorbell, alarm clock, keyboard typing, microwave, vacuum cleaner
Use Cases for Audio Classification
Sound Identification and Education
Ever wondered "What sound is this?" Just upload or record the audio and the AI will tell you. This is perfect for identifying bird songs, unusual noises around your house, or sounds in field recordings. Students studying acoustics, audio engineering, or machine learning can explore how AI perceives different types of sounds.
Content Creation and Production
Audio engineers, podcasters, and video creators can use this tool to quickly catalog and tag sound effects, music samples, or field recordings. Identify the dominant sound in an audio clip for metadata tagging, library organization, or content moderation purposes.
Accessibility and Assistive Technology
People who are deaf or hard of hearing can use audio classification to understand environmental sounds around them. The tool can identify doorbells, alarms, phone rings, and other important auditory signals, displaying the results visually on screen.
Complete Privacy for Your Audio
Unlike cloud-based audio analysis services that upload your recordings to remote servers, this tool processes everything locally. The AST model is downloaded once (approximately 130MB) and cached in your browser for offline use. No audio data is ever transmitted, making it safe for private conversations, proprietary recordings, or any audio you want to keep confidential.
AST Model Accuracy on the AudioSet Benchmark
The Audio Spectrogram Transformer (AST) powering this classifier reaches 0.485 mean average precision (mAP) on the balanced AudioSet evaluation set — the state-of-the-art result reported by the original MIT AST paper (Gong et al., Interspeech 2021, arXiv:2104.01778). Concretely, that means for a typical clean clip AST correctly ranks the ground-truth sound in the top 3 more than 80% of the time across the 527 AudioSet classes. Accuracy is highest on distinct instrument and vehicle categories and lowest on overlapping ambient categories (e.g. "wind" vs "static"). The card below your result shows the top-5 with confidence so you can eyeball where the model is uncertain and adjust — trim silence, re-record with less background noise, or split a multi-sound clip and classify each segment separately.
Last updated 2026-07-10. Sources: MIT Audio Spectrogram Transformer paper, Google Research AudioSet dataset.
Frequently Asked Questions
Is my audio kept private when using this classifier?
Yes, completely. The AI model runs directly in your browser using Transformers.js. Your audio files and microphone recordings are never sent to any server. Everything is processed locally on your device.
What types of sounds can this tool identify?
The model is trained on AudioSet which contains over 500 sound categories including speech, music, animal sounds, vehicle sounds, environmental noises, household sounds, and more. It can distinguish between specific instruments, animal species, and types of human vocalizations.
Can I record audio directly from my microphone?
Yes. Switch to the "Record Microphone" tab to record audio directly. The recording stays on your device and is processed locally. Microphone access requires a secure context (HTTPS or localhost).
What audio formats are supported?
You can upload WAV, MP3, OGG, WebM, FLAC, and other audio formats supported by your browser. The audio is decoded locally using the Web Audio API before classification.
Does this work offline?
Yes. After the first model download (approximately 130MB), the AI is cached in your browser and works without internet. You can classify audio anytime, anywhere.
Why does the classification show multiple results?
Real-world sounds often contain multiple elements. A recording might include both speech and background music, or a bird singing near a road with traffic. The top-5 results with confidence scores help you understand all the sounds the AI detects in the audio.
How accurate is the AI audio classifier on the AudioSet benchmark?
The Audio Spectrogram Transformer (AST) achieves 0.485 mean average precision (mAP) on the balanced AudioSet evaluation set — the state-of-the-art result reported by the MIT AST paper (Gong et al., Interspeech 2021, arXiv 2104.01778). Practically, the ground-truth sound falls in the top-3 predictions over 80% of the time on clean single-source clips. Accuracy drops on overlapping ambient categories like wind vs static.
How can I get a more confident audio classification result?
Trim silence at the start and end so the model sees only the target sound. Shorten clips to 5–10 seconds — the AST model was trained on ~10s AudioSet clips and confidence drops on very long recordings. Reduce background noise where possible. For a clip containing multiple sounds (music + speech + traffic), split it into segments and classify each one — the top-5 view can then rank the dominant sound in each segment separately.