Relative Mode

This package contains Python code for calculating relative mode from an audio signal. Relative mode refers to the degree between how major or minor does the segment of music sound at a given time. It is based on a classic key-finding algorithm (Krumhansl-Schmuckler, 1990) and extracts the pitch-class information using chromagrams. The relative mode is calculated as the difference between the strongest major key and the strongest minor key. Relative mode can vary from -1.0 (clearly in minor) to + 1.0 (clearly in major) and gives a value between these extremes for the whole excerpt. Alternatively the algorithm can provide the output for each window of analysis (segments of 3 seconds as a default).

The algorithm and how it is evaluated is fully documented in a manuscript titled “Major-minorness in Tonal music – Evaluation of Relative Mode Estimation using Expert Ratings and Audio-Based Key-finding Principles” by Tuomas Eerola and Michael Schutz (Psychology of Music, in press).

Libraries

import librosa.display
import matplotlib
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd

Load package using pip

pip install relative_mode

Make function calls explicit for the subsequent analyses.

from src.relative_mode import Tonal_Fragment
from src.relative_mode import relative_mode
from src.relative_mode import RME_across_time

Load a music example

Recording of J.S. Bach’s C Major Prelude (WTC Book I) (an extract).

filename = 'data/Bach_1_Gould_0_Major_bachGould1971.wav'
y, sr = librosa.load(filename)
plt.figure(figsize=(9,2.5))
librosa.display.waveshow(y, sr = sr)
plt.show()

Estimate relative mode

Here we don’t specify any parameters but just run relative_mode.

RM, RM_segments = relative_mode(y = y, sr = sr)
print(round(RM['tondeltamax'][0],3))

0.258

The value of 0.258 could be called “moderately in major”. Value closer to 0 would indicate no clear tendency for major or minor and any value below -0.30 would suggest clearly in minor key.

This measure can be computed with a different options. You can alter key profile (e.g. krumhansl, albrecht (default), aarden, or bellman), similarity metrics (pearson, cosine (default), or euclidean), chromatype from CENS to CQT. There are also some alternative outputs of the measure.

RM2, RM2_segments = relative_mode(y = y, sr = sr, profile = 'simple', distance = 'pearson', chromatype = 'CQT')
print(RM2)

   tonmaxmaj  tonmaxmin  tondeltamax  tondeltamaxMd  tondeltamaxSi
0   0.688322   0.640134     0.144563       0.176965            3.0

Estimate relative mode across the excerpt

fig, RM3 = RME_across_time(filename = filename, winlen = 3, hoplen = 3, cropfirst = 0, croplast = 0, chromatype = 'CENS', profile = 'albrecht', distance = 'cosine', plot = True)
fig
plt.show()

Alternative analyses

In the article (Eerola & Schutz, 2025), we assess various parameters of the model (key profiles, distance measures, alternative formulations of the model) in Experiment 1. We also examine what could explain the variations in model success across recordings used in Experiment 3. Here we briefly report these alternative explorations.

Experiment 1: Alternative analyses

The model compares the difference between highest maximum major key strength and the maximum minor key strength. We also have two alternative formulations of the model, one that utilises comparison with the parallel minor and another one relying on the relative minor.

The parallel minor key of the major key received lower correlation with the expert ratings (r = 0.698) than the actual model (r = 0.840). The second alternative relies on the relative minor key of the major key. This alternative received a lower correlation (r = 0.766) with the expert ratings compared to the proposed model. For this reason, we did not pursue these two alternatives further.

We also run alternative formulations of window length (1 to 5 seconds) and overlap (0 to 75% overlap) which did not provide substantially better fit with the data. Finally, the way of summarizing the RME across the analysis windows with the mean values did not appear to be significantly different from the taking median of the predictions within the analysis windows (r = 0.785).

Experiment 3: Alternative analyses

To identify the consistent noise factors in the RME analysis from audio, we extracted dynamics, several timbral descriptors (brightness, spectral centroid, spectral flux, rms, roughness) and tempo descriptors for each excerpt using Essentia, and added these as additional predictors to the regression with RME model predicting the expert ratings. However, no single audio descriptor could contribute significantly (more than 2 % of the variance accounted) to the model that already had a highly successful predictor (RME) within it. A more extensive analysis of the potential additional considerations would benefit from a larger set of materials and from systematic alterations of the most plausible variations of these factors.

References

Eerola, T. & Schutz, M. (in press). Major-minorness in Tonal music – Evaluation of Relative Mode Estimation using Expert Ratings and Audio-Based Key-finding Principles. Psychology of Music, X(xx), xxx-xxx.