Uwarp

Upload an audio file to see pitch analysis and a note contour graph.

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Audio file pitch detector

Upload MP3, WAV, or M4A to detect pitch, notes, and Hz locally in your browser. See a pitch curve, cents deviation, and range stats—no server upload.

What is an audio file pitch detector?

An audio file pitch detector reads a recording you already made and identifies musical notes, frequencies in Hz, and cents deviation over time—without a live microphone. Upload MP3, WAV, or M4A and this note detector analyzes the file locally in your browser, then shows a pitch contour graph plus summary stats. For live Hz readings from your mic, use /frequency-detector instead.

Pitch detector features for audio files

Upload a recording and review note names, Hz, and pitch movement.

  • MP3, WAV, and M4A upload

    Drag and drop or pick a file up to 50 MB—common voice-memo and export formats.

  • Note and Hz output

    Each voiced segment maps to a note name, exact Hz, and cents from equal temperament.

  • Pitch contour graph

    Hover the curve to inspect pitch at any moment in the analyzed section.

  • Range and average stats

    See common note, average frequency, detected range, and unique note count.

  • Local browser processing

    Audio decodes and analyzes on your device—files are not uploaded to a server.

  • Built-in playback

    Listen to the same file while you read the detected pitch data.

How to detect pitch from an audio file

Analyze a recording in four steps.

  1. Upload your file

    Select or drop MP3, WAV, M4A, or AAC. Use a clear solo vocal or single-instrument take when possible.

  2. Wait for local analysis

    The tool decodes the file and scans short windows for fundamental frequency.

  3. Read the pitch curve

    Hover the graph to see note, Hz, and cents at each point. Flat lines mean stable pitch.

  4. Copy or upload another

    Copy the summary for notes, or reset to analyze a different recording.

Tips for better pitch detection

Improve note detector accuracy on uploaded audio.
  • 01

    Use monophonic audio

    One voice or one instrument at a time works best. Full mixes confuse the detector.

  • 02

    Prefer WAV when you can

    Uncompressed WAV often gives cleaner readings than low-bitrate MP3.

  • 03

    Reduce background noise

    Quiet rooms and dry recordings produce more voiced frames on the graph.

  • 04

    Long files analyze the first two minutes

    Very long recordings analyze the opening section to keep the browser responsive.

  • 05

    Pair with live tools

    Practice with /frequency-detector on mic, then review takes here—or train recall on /sound-memory-game.

  • 06

    Not pitch correction

    This tool diagnoses pitch; it does not change or export corrected audio.

Sources & References

Authoritative references on pitch detection, audio analysis algorithms, and the Web Audio technology this tool uses.
  • “A pitch detection algorithm is a computational algorithm designed to estimate the pitch or fundamental frequency of a quasiperiodic or virtually periodic audio signal.”
    — Wikipedia — Pitch detection algorithm

    Pitch detection algorithm (PDA): This tool uses a time-domain pitch detection algorithm (YIN) adapted from the autocorrelation method to estimate the fundamental frequency of voiced audio frames from uploaded MP3, WAV, or M4A files. en.wikipedia.org/wiki/Pitch_detection_algorithm

  • “The fundamental frequency, often referred to simply as the fundamental, is the lowest frequency of a periodic waveform. In music, the fundamental determines the perceived pitch of a note.”
    — Wikipedia — Fundamental frequency

    Fundamental frequency and pitch perception: The tool scans short audio windows for the fundamental frequency, maps it to the nearest equal-temperament note name, and reports the exact Hz value along with cents deviation. en.wikipedia.org/wiki/Fundamental_frequency

  • “The cent is a logarithmic unit of measure used for musical intervals. Twelve-tone equal temperament divides the octave into 12 semitones each 100 cents in width.”
    — Wikipedia — Cent (music)

    Cents — logarithmic unit of pitch interval: Cents deviation shows how far a detected pitch is from the nearest equal-temperament note. A value of 0 means perfectly in tune; positive or negative values indicate sharpness or flatness. en.wikipedia.org/wiki/Cent_(music)

  • “The Web Audio API provides a powerful and versatile system for controlling audio on the Web, allowing developers to choose audio sources, add effects, create audio visualizations, and more.”
    — MDN Web Docs — Web Audio API

    Web Audio API (browser audio decoding and analysis): The file is decoded client-side using the Web Audio API's AudioContext.decodeAudioData method. All pitch analysis runs on the decoded PCM buffer in memory — files are never uploaded to a server. developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API

  • “Autocorrelation is the correlation of a signal with a delayed copy of itself as a function of delay. It is widely used in pitch detection to find the period of a periodic signal.”
    — Wikipedia — Autocorrelation

    Autocorrelation for pitch tracking: The YIN pitch detection algorithm relies on a modified autocorrelation function to find the fundamental period of an audio frame, giving higher accuracy than simple zero-crossing or cepstral methods. en.wikipedia.org/wiki/Autocorrelation

  • “Equal temperament is a musical tuning system in which the frequency interval between every pair of adjacent notes has the same ratio, dividing the octave into 12 equal semitones.”
    — Wikipedia — Equal temperament

    Equal temperament tuning system: Detected Hz values are mapped to the nearest note in the A4 = 440 Hz equal-temperament scale. The tool reports note names (C, C#, D, etc.) and the cents difference from the ideal equal-temperament frequency. en.wikipedia.org/wiki/Equal_temperament

Frequently Asked Questions

Quick answers about this tool—open a question to read more.