Get Track's Audio Analysis
Get a low-level audio analysis for a track in the Spotify catalog. The audio analysis describes the track’s structure and musical content, including rhythm, pitch, and timbre.
Parameters
Returns
Each segment contains a roughly conisistent sound throughout its duration.
Pitch content is given by a “chroma” vector, corresponding to the 12 pitch classes C, C#, D to B, with values ranging from 0 to 1 that describe the relative dominance of every pitch in the chromatic scale. For example a C Major chord would likely be represented by large values of C, E and G (i.e. classes 0, 4, and 7).
Vectors are normalized to 1 by their strongest dimension, therefore noisy sounds are likely represented by values that are all close to 1, while pure tones are described by one value at 1 (the pitch) and others near 0.
As can be seen below, the 12 vector indices are a combination of low-power spectrum values at their respective pitch frequencies.
Timbre is the quality of a musical note or sound that distinguishes different types of musical instruments, or voices. It is a complex notion also referred to as sound color, texture, or tone quality, and is derived from the shape of a segment’s spectro-temporal surface, independently of pitch and loudness. The timbre feature is a vector that includes 12 unbounded values roughly centered around 0. Those values are high level abstractions of the spectral surface, ordered by degree of importance.
For completeness however, the first dimension represents the average loudness of the segment; second emphasizes brightness; third is more closely correlated to the flatness of a sound; fourth to sounds with a stronger attack; etc. See an image below representing the 12 basis functions (i.e. template segments).
The actual timbre of the segment is best described as a linear combination of these 12 basis functions weighted by the coefficient values: timbre = c1 x b1 + c2 x b2 + ... + c12 x b12, where c1 to c12 represent the 12 coefficients and b1 to b12 the 12 basis functions as displayed below. Timbre vectors are best used in comparison with each other.
Get Track's Audio Analysis
package dev.cjav.spotted.example
import dev.cjav.spotted.client.SpottedClient
import dev.cjav.spotted.client.okhttp.SpottedOkHttpClient
import dev.cjav.spotted.models.audioanalysis.AudioAnalysisRetrieveParams
import dev.cjav.spotted.models.audioanalysis.AudioAnalysisRetrieveResponse
fun main() {
val client: SpottedClient = SpottedOkHttpClient.fromEnv()
val audioAnalysis: AudioAnalysisRetrieveResponse = client.audioAnalysis().retrieve("11dFghVXANMlKmJXsNCbNl")
}{
"bars": [
{
"confidence": 0.925,
"duration": 2.18749,
"published": true,
"start": 0.49567
}
],
"beats": [
{
"confidence": 0.925,
"duration": 2.18749,
"published": true,
"start": 0.49567
}
],
"meta": {
"analysis_time": 6.93906,
"analyzer_version": "4.0.0",
"detailed_status": "OK",
"input_process": "libvorbisfile L+R 44100->22050",
"platform": "Linux",
"status_code": 0,
"timestamp": 1495193577
},
"published": true,
"sections": [
{
"confidence": 1,
"duration": 6.97092,
"key": 9,
"key_confidence": 0.297,
"loudness": -14.938,
"mode": -1,
"mode_confidence": 0.471,
"published": true,
"start": 0,
"tempo": 113.178,
"tempo_confidence": 0.647,
"time_signature": 4,
"time_signature_confidence": 1
}
],
"segments": [
{
"confidence": 0.435,
"duration": 0.19891,
"loudness_end": 0,
"loudness_max": -14.25,
"loudness_max_time": 0.07305,
"loudness_start": -23.053,
"pitches": [
0.212,
0.141,
0.294
],
"published": true,
"start": 0.70154,
"timbre": [
42.115,
64.373,
-0.233
]
}
],
"tatums": [
{
"confidence": 0.925,
"duration": 2.18749,
"published": true,
"start": 0.49567
}
],
"track": {
"analysis_channels": 1,
"analysis_sample_rate": 22050,
"code_version": 3.15,
"codestring": "codestring",
"duration": 207.95985,
"echoprint_version": 4.15,
"echoprintstring": "echoprintstring",
"end_of_fade_in": 0,
"key": 9,
"key_confidence": 0.408,
"loudness": -5.883,
"mode": 0,
"mode_confidence": 0.485,
"num_samples": 4585515,
"offset_seconds": 0,
"rhythm_version": 1,
"rhythmstring": "rhythmstring",
"sample_md5": "sample_md5",
"start_of_fade_out": 201.13705,
"synch_version": 1,
"synchstring": "synchstring",
"tempo": 118.211,
"tempo_confidence": 0.73,
"time_signature": 4,
"time_signature_confidence": 0.994,
"window_seconds": 0
}
}Returns Examples
{
"bars": [
{
"confidence": 0.925,
"duration": 2.18749,
"published": true,
"start": 0.49567
}
],
"beats": [
{
"confidence": 0.925,
"duration": 2.18749,
"published": true,
"start": 0.49567
}
],
"meta": {
"analysis_time": 6.93906,
"analyzer_version": "4.0.0",
"detailed_status": "OK",
"input_process": "libvorbisfile L+R 44100->22050",
"platform": "Linux",
"status_code": 0,
"timestamp": 1495193577
},
"published": true,
"sections": [
{
"confidence": 1,
"duration": 6.97092,
"key": 9,
"key_confidence": 0.297,
"loudness": -14.938,
"mode": -1,
"mode_confidence": 0.471,
"published": true,
"start": 0,
"tempo": 113.178,
"tempo_confidence": 0.647,
"time_signature": 4,
"time_signature_confidence": 1
}
],
"segments": [
{
"confidence": 0.435,
"duration": 0.19891,
"loudness_end": 0,
"loudness_max": -14.25,
"loudness_max_time": 0.07305,
"loudness_start": -23.053,
"pitches": [
0.212,
0.141,
0.294
],
"published": true,
"start": 0.70154,
"timbre": [
42.115,
64.373,
-0.233
]
}
],
"tatums": [
{
"confidence": 0.925,
"duration": 2.18749,
"published": true,
"start": 0.49567
}
],
"track": {
"analysis_channels": 1,
"analysis_sample_rate": 22050,
"code_version": 3.15,
"codestring": "codestring",
"duration": 207.95985,
"echoprint_version": 4.15,
"echoprintstring": "echoprintstring",
"end_of_fade_in": 0,
"key": 9,
"key_confidence": 0.408,
"loudness": -5.883,
"mode": 0,
"mode_confidence": 0.485,
"num_samples": 4585515,
"offset_seconds": 0,
"rhythm_version": 1,
"rhythmstring": "rhythmstring",
"sample_md5": "sample_md5",
"start_of_fade_out": 201.13705,
"synch_version": 1,
"synchstring": "synchstring",
"tempo": 118.211,
"tempo_confidence": 0.73,
"time_signature": 4,
"time_signature_confidence": 0.994,
"window_seconds": 0
}
}