Upload Audio and Get an AI Description of What’s in It
Upload an audio file or paste a link, and the AI listens to it and describes what it hears in plain words: what kind of sounds are in the recording, what instruments or genre it sounds like if there’s music, how many people are speaking, and roughly what they’re talking about. It also points out anything that stands out as a quality problem, like background noise or muffled speech.
7,102 people used ScreenApp’s audio analyzer in the last 90 days. This is an AI’s read on the file, in plain language, not a lab measurement. Alongside the description you get the full transcript, so you can check anything the AI says against the words themselves.
What you get:
- A plain-language description of what’s in the file: the kind of sounds, instruments and likely genre if there’s music
- How many people are speaking, with a transcript in 100+ languages
- A summary of what the speakers talk about
- Notes on obvious quality problems, described in plain words rather than a number
If you want the number rather than the description, that is a different tool. The audio quality checker measures the waveform in your browser and reports peak level, clipping, noise floor and signal-to-noise in decibels. Use that one to decide whether a recording is technically clean, and this one to find out what is actually in it.
- Ask the AI follow-up questions about the recording in chat
- Free plan: 2 transcriptions of recordings up to 45 minutes each, with AI chat
How to Get an AI Sound Recognition Report
- Upload or link the file: drag in MP3, M4A, MP4A, M4B, AAC, WAV, OGG, OPUS, FLAC, AIFF, WMA, WEBMA, MKA, AC3, EAC3, WV, AMR, DSF, DFF (up to 2 GB in the free online tool), or paste a link to a hosted file.
- Read the AI’s description: a few minutes later you get the plain-language description, the speaker count, and the transcript.
- Ask follow-up questions: use AI chat to ask about a specific part of the recording, or jump straight to the transcript.
A clean recording with one or two voices gets a clearer read than a noisy room with overlapping speech and background music. See how we measure transcript accuracy.
AI Sound Recognition: ScreenApp vs Other Apps
| Feature | ScreenApp | Otter.ai | AssemblyAI |
|---|---|---|---|
| Free tier | 2 transcriptions of recordings up to 45 minutes each | 300 minutes/month | $50 in credit |
| Price | $19/month annual | $8.33/month annual | $0.15/hour of audio |
Sources, checked 2026-09-28: ScreenApp pricing, otter.ai/pricing, assemblyai.com/pricing
How they differ in practice:
- Otter.ai transcribes meetings and lectures with speaker labels, but its own site does not describe identifying music, instruments or non-speech sounds. ScreenApp adds that description on top of the transcript.
- AssemblyAI is a developer API, priced at $0.15 per hour of audio, that other companies build products with. It is not something you upload a file to directly and read a report from, and its documentation does not describe music or ambient sound detection.
Who Uses an AI Sound Recognition Tool
Podcasters check an episode before it goes out. The AI’s description flags a chair creak under dialogue or a moment where background noise picks up, so the editor knows where to look.
Field recordists and archivists label clips they did not record themselves. Instead of listening to every file, they read the AI’s description of whether a clip is rain, traffic, a crowd or an instrument, and confirm it by ear.
Support and QA teams review call recordings to see how many people were on the line and what the call was about, without listening to the whole thing first.
Researchers and journalists working with long interview recordings get a summary of who spoke and what came up, then use the transcript to pull an exact quote.
Content moderators check what is actually in a submitted audio clip, a description they can act on alongside the transcript, before deciding what to do with it.
