Drop an audio file in, and the tool tells you what is in it. The model separates music from speech, flags ambient sounds (traffic, wind, room tone, HVAC hum), names instruments where it can (acoustic guitar, kick drum, synth pad), and detects the spoken language. Output arrives as a tagged timeline, not a wall of waveforms.
AI Audio Analyzer - Voice Analysis Online
Upload an MP3, WAV, FLAC, M4A, OGG, or AAC file up to 500MB. The AI scans the track and returns a content map: where speech occurs, where music plays, where silence or noise dominates, and which voices belong to which speaker.
What it picks up:
- Speech segments with language detection across 99 languages
- Music sections tagged by genre, tempo, and dominant instruments
- Ambient categories: indoor room tone, outdoor traffic, crowd noise, mechanical hum, weather
- Speaker count with per-voice timestamps (diarization)
- Pitch range, vocal tone, and emotion cues per speaker
- Audio defects: clipping, plosives, sibilance, hum at 50/60Hz, hiss
Each detected event carries a confidence score and a start/end timestamp. Music recognition uses fingerprint matching against published catalogues, so a 10-second snippet of a licensed track gets flagged with the title where a match exists. The analyser also produces frequency distribution, dynamic range, and loudness measurements (LUFS) for the file as a whole.
AI Sound Analyzer and Sound Identifier
The sound identifier classifies audio sources against a labelled training set covering thousands of categories. Useful detection groups include:
- Human sounds: speech, laughter, coughing, crying, applause, footsteps
- Music: genre tags, instrument families, vocal vs instrumental, BPM estimate
- Animal sounds: dog barks, bird calls (broad family level), cat meows
- Mechanical: engine noise, fan hum, keyboard typing, door slams
- Environmental: rain, wind, water, fire crackle, thunder
A report lists every category found, the seconds where it appears, and a confidence value. For tracks that contain recognisable commercial music, audio fingerprinting tries to name the title and rights holder so reviewers can act before publication.
Audio Analyzer vs Other Tools
| Feature | ScreenApp | Auphonic | Adobe Podcast Enhance | AudioStrip | Krisp | ACRCloud |
|---|---|---|---|---|---|---|
| Identifies music / speech / noise | Yes (tagged timeline) | Speech vs music split | Speech focus | Vocals vs instrumental | Speech vs noise only | Yes (music + speech) |
| Music recognition (title matching) | Yes (fingerprint) | No | No | No | No | Yes (primary use case) |
| Noise removal | Tagged with timestamps | Adaptive leveler + denoise | One-click enhance | Stem isolation | Real-time suppression | No (recognition only) |
| Speech enhancement | Pitch, clarity, defects report | Loudness + filtering | Studio-quality remaster | Limited | Real-time clean voice | No |
| File size limit | 500MB | 500MB (Pro) | ~1GB / 1hr | 50MB free, 1GB paid | Real-time stream | API-driven, per-request |
| Pricing | $19/month annual | EUR 11/month (Pro) | Free beta | $9.99/month | $8/month annual | Pay-as-you-go API |
| Output | Timeline + confidence scores | Cleaned WAV/MP3 | Cleaned WAV/MP3 | Stems (vocal/instr.) | Cleaned audio stream | JSON match results |
| Best for | Diagnosing what is in a file | Podcast post-production | Quick podcast clean-up | Vocal isolation / remixing | Calls and meetings | Music ID and royalty tracking |
How they differ in practice:
- Auphonic cleans and levels podcast audio but does not name music tracks or label ambient categories.
- Adobe Podcast Enhance fixes speech recordings; it has no music identification or sound classification report.
- AudioStrip splits a track into vocal and instrumental stems. It does not identify what the instruments are or detect ambient sound.
- Krisp suppresses noise during live calls. It does not output a content map of an uploaded file.
- ACRCloud excels at naming commercial music via fingerprint, but it is an API for developers and does not produce a human-readable analysis page or speech defect report.
ScreenApp covers the middle ground: tell me what is in this file, where it occurs, who is speaking, and what might be wrong with the recording.
How to Use the Audio Analyzer
Drag and drop MP3, WAV, or any audio format into the browser for instant analysis.
- Upload your file (any format, up to 500MB)
- Pick the analysis you want: content map, voice report, or quality check
- The AI processes the file with spectrum analysis and sound recognition
- Review the tagged timeline, speaker list, and defect log
- Download reports or share results with your team
The tool handles bitrates from 32kbps to 320kbps. Voice reports include pitch, vocal characteristics, and speaker ID. Sound analysis covers frequency distribution, dynamic range, and quality scoring. Spectrograms, waveforms, and frequency charts generate automatically. All processing runs on encrypted servers.
Who Uses an AI Voice Analyzer and Sound Analyzer
Podcasters QA-ing Recordings
Before publishing an episode, podcasters run the file through to catch problems they missed in editing: a chair creak under dialogue, a refrigerator hum in the room tone, a guest whose audio clips during laughs. The defect log lists timestamps so the editor can jump straight to the spot.
Sound Designers Identifying Samples
A designer working with field recordings or sample-library hand-offs uses the classifier to label unknown clips: is this rain or applause, a vintage synth or a brass section, indoor or outdoor space. Saves rebuilding metadata by ear.
Music Supervisors Clearing Rights
When a rough cut comes back with placeholder music, the supervisor uploads the audio to spot any commercial tracks accidentally left in. Fingerprint matches name the song and label so the team can either license it or replace it.
Audio Engineers Diagnosing Problem Recordings
Engineers troubleshooting a bad recording get a fast read on what went wrong: a 60Hz ground loop, a phase issue between two mics, a low-frequency rumble from traffic, sibilance from a specific speaker. The frequency report points to the cause instead of guessing.
Copyright-Claim Reviewers
Teams handling DMCA disputes or platform claims need to verify what audio is actually in a clip. The identifier flags music matches, isolates the timestamps in question, and produces a written report suitable for evidence packets.
FAQ
What is a voice analyzer and how does it work?
A voice analyzer uses AI to examine vocal characteristics including pitch, tone, accent, emotion, and speaker identity. It processes files automatically to detect quality issues, identify speakers, and generate a structured report.
How do I identify this sound online free?
Upload your file to the sound identifier and the AI will identify it within 30-60 seconds. It recognizes thousands of environmental sounds, music elements, and voice patterns free with basic features.
How accurate is the AI voice detector?
It analyzes pitch, tone, accents, and background noise, and flags low-confidence sections so you can spot-check them. Treat it as an automated first pass, not a lab-grade measurement.
Can the sound identifier detect copyright material?
Yes. Audio fingerprinting identifies potential matches against major music and sound effect libraries, helping creators avoid copyright strikes before publishing.
Does the audio analyzer work with all formats?
It supports MP3, WAV, FLAC, M4A, OGG, and AAC at bitrates from 32kbps to 320kbps, up to 500MB per file.
Can the voice analyzer detect different speakers?
Yes. The AI distinguishes between voices using speaker diarization, which works for podcast analysis, meeting recordings, and voice recognition.
Is audio analysis safe and private?
Yes. Files are encrypted with 256-bit encryption and deleted automatically after 24 hours. The tool does not store or share your audio.
Can I analyze audio from video files?
Yes. Upload MP4, MOV, or other video files and the tool extracts and analyzes the audio track automatically, covering voice quality, background sounds, and levels.
How do I analyze audio file quality?
Upload your file and the AI examines frequency distribution, dynamic range, clipping, noise floor, and compression. You get quality scores with specific recommendations.
How does this compare to running audio through ChatGPT?
Text-only chatbots have no native path for analysing an uploaded audio file. This tool ingests the file directly and returns timestamped detections for music, speech, ambient sound, instruments, and language, plus a defect report.
