How good is my audio quality? Upload a clip for AI grading of clarity, noise, and frequency balance.
Select the AI model for audio analysis. Different models may have different capabilities.
Record audio directly from your microphone
Audio Quality Analyzer is a professional AI-powered tool that evaluates technical aspects of audio including noise levels, clarity, frequency balance, dynamic range, and production quality. It analyzes audio quality metrics to identify technical issues or artifacts and suggest improvements for audio quality. The tool examines clarity, noise levels, frequency balance, dynamic range, stereo image, and overall production quality, providing detailed analysis of technical quality. This makes it valuable for audio engineers mixing and mastering, content creators ensuring quality, podcasters maintaining production standards, or anyone wanting to assess and improve audio quality.
Upload your audio content and the AI analyzes technical quality systematically. It evaluates clarity and how clearly audio is reproduced. Noise analysis examines background noise levels and quality. Frequency balance assessment looks at how frequencies are distributed. Dynamic range evaluation examines the range between quietest and loudest parts. Stereo image analysis evaluates spatial audio characteristics. Production quality assessment looks at overall technical quality. The tool provides detailed analysis of each aspect, identifies technical issues or artifacts, explains what affects quality, and suggests improvements. It offers specific recommendations for enhancing audio quality, making it valuable for production work. You can specify particular technical aspects you want analyzed in the notes field.
Upload a clip and the AI grades it across the dimensions that decide whether audio sounds professional: clarity, noise floor, frequency balance, dynamic range, stereo image, and overall production polish. Each issue it finds comes with a suggested fix, so the answer is less a verdict and more a punch list.
That diagnosis is the whole point. Bad is usually one of a handful of specific problems: room echo, low-frequency rumble, harsh sibilance, a muddy low-mid buildup, clipping, or aggressive noise reduction artifacts. The analysis names which ones are present in your clip and where, which beats guessing with random EQ moves.
Run an episode segment through it and you get a direct answer against the things listeners punish: noise under the voice, inconsistent levels, boxy room sound, harshness that fatigues ears on long listens. Spoken word has different priorities than music (intelligibility over everything), and the suggestions reflect that.
Both. A diagnosis like muddy low mids comes with the corresponding move: cut around 250 to 400 Hz, tame the room, back off the compressor. Suggestions are phrased to be actionable in any DAW or editor. You fix, re-upload, and compare, which is the cheapest second opinion loop in audio.
It is a perceptual review, not a lab measurement. For exact LUFS targets, true peak values, or broadcast compliance numbers, use a meter; the AI describes what it hears the way an experienced engineer would on a listen-through. The two complement each other: meters give numbers, this explains which audible problems matter and what causes them.
Record a minute of typical content in your normal space with your normal settings, then upload that rather than a cherry-picked best take. Testing the realistic case surfaces the issues that actually reach your audience. If you are comparing mics or rooms, run one clip of each through and the differences become very concrete.
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