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Sound Quality Analyzer (Sound Quality Evaluation & Psychoacoustic Analysis)

Sound Quality Analyzer

Overview

This tool is used to quantify how sound is perceived by the human ear ("subjective quantity"). Instead of simply measuring voltage or sound pressure, it uses metrics based on psychoacoustics to objectively evaluate the pleasantness or unpleasantness of a sound.

This tool is for offline analysis only. It analyzes pre-recorded audio files.

☕ Coffee Break: How do we turn "Good Sound" into numbers?

Normal measuring instruments (like oscilloscopes) measure the physical properties of electricity, such as "voltage" and "frequency." In cooking terms, this is like measuring "how many grams of salt are in it" or "what the temperature is." But we don't feel something is "delicious!" just based on the amount of salt, do we? We only feel it's "delicious" when the balance of sweetness and sourness, temperature, and texture all come together.

Sound is exactly the same. Just knowing "how many decibels a sound is" doesn't tell us whether it's "pleasant music" or "the annoying sound of scratching a chalkboard." The field of "Psychoacoustics" studies the "perceptual quirks" of the human ear and brain. This Sound Quality Analyzer is not just an electrical meter, but a "taste sensor for sound" that evaluates "how humans feel when they hear it"! Because it translates whether a sound is "grating (Roughness)" or "piercing (Sharpness)" into numbers, it's very useful for designing product motor noises or alarm sounds to be "more elegant."

Metric Descriptions

  • Integrated Loudness: An average value of "sound volume" that takes into account the sensitivity characteristics of the human ear (BS.1770 K-weighting). The unit is LUFS.
  • Sharpness: Represents the "sharpness" or "metallic" quality of a sound. Higher values indicate more high-frequency components (typically above 15.8 Bark). The unit is acum.
  • Roughness: Represents the "graininess" or "roughness" of a sound. It evaluates unpleasant modulations (around 70 Hz, for example) that cause a sensation of "roughness." The unit is asper.
  • Tonality: Represents the extent to which the sound contains "sine-wave-like components" (Spectral Flatness). Sounds like white noise have low tonality, while sounds like a whistle or a pure sine wave approach 1.0. The unit is 0-1 (normalized value).
  • Fluctuation Strength: Similar to roughness, it represents the "modulation" or "fluctuation" of a sound, but for slower changes (typically below 20 Hz, peaking around 4 Hz). The unit is vacil.
  • Articulation Index (AI): A metric representing "speech intelligibility" in the presence of noise. It ranges from 0.0 to 1.0, where 1.0 means perfect intelligibility.

Operation

  1. Click Load File to select an audio file.
    • Supported formats: WAV, FLAC, AIFF
    • There is a file size limit of 500 million total samples (approx. 1 hour 26 mins for 48kHz Stereo).
  2. Press the Analyze button to start the analysis.
    • Internally, the audio is resampled to 48kHz for analysis (to optimize psychoacoustic filters).
    • Long files may take some time to process.
  3. Once the analysis is complete, the Summary Metrics will display the average values for each channel (Loudness, Sharpness, Roughness, Tonality, Fluctuation Strength, AI) in a table format.
  4. Click the Export CSV button to save the analysis results (including average metrics and time-series data) as a CSV file.
  5. The graphs below show how each of these metrics "changed over time." Use the tabs to switch between metrics.

Playback and Verification

  • Playback Button (▶): Plays the analyzed audio file (follows the GUI audio engine settings).
  • Follow Cursor: When checked, the yellow cursor on the graph moves in synchronization with the playback. You can listen to the sound at specific "high value (or discontinuous) locations."
  • Graph Interaction: Click on the graph to move the playback cursor to that position.

Use Cases

  • Analysis of Unpleasant Noise: Quantifies "why" fan or motor noise is annoying using metrics like roughness and sharpness.
  • Sound Design Evaluation: Verifies if product operation sounds or notification sounds match the intended image (e.g., gentle, sharp, powerful).
  • Detection of Abnormal Sounds: Detects sudden changes in tonality (e.g., occurrence of a beep) within stationary noise.