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README.md

README.md

Comparison of Automated Mastering Services

Abstract

We proposed an objective mix evaluation index called MixEvaluationIndex20190207(or MEI20190207) that is constructed based on subjective mix evaluation data retrieved from THE MIX EVALUATION DATASET.

We compared the mastered audio quality of automated mastering services (AI Mastering and LANDR) using various indicies including MEI20190207.

AI Mastering seems better than LANDR from the perspective of MEI20190207. However, there is a need to further study about the validity of MEI20190207 because of dataset bias (e.g. all mixes in datasets have high dynamic range compared to mastered audios).

Please check if mastered audios that have high MixEvaluationIndex20190207 are actually good.

Comparison Targets

Result Summary

MixEvaluationIndex20190207 Change

MixEvaluationIndex20190207 Change

Loudness vs Loudness Range

Loudness vs Loudness Range

True Peak

True Peak

Dissonance Change

Dissonance Change

Hardness Change

Hardness Change

more data

All results are available in stats directory.

Audio Comparison

InTheMeantime

Service/Settings MEI20190207 Rank MEI20190207 Audio
Original 13 0.6568856051215048 InTheMeantime
AI Mastering (level 0.0, -12.0dB) 6 0.8903143559671387 InTheMeantime
AI Mastering (level 0.0, -10.0dB) 10 0.8731447561481194 InTheMeantime
AI Mastering (level 0.0, -8.0dB) 7 0.8833304362146213 InTheMeantime
AI Mastering (level 0.5, -12.0dB) 2 0.9850537690088825 InTheMeantime
AI Mastering (level 0.5, -10.0dB) 1 0.9945873666852472 InTheMeantime
AI Mastering (level 0.5, -8.0dB) 3 0.9115010315417398 InTheMeantime
AI Mastering (level 1.0, -12.0dB) 11 0.8712285143889935 InTheMeantime
AI Mastering (level 1.0, -10.0dB) 8 0.8766534265851891 InTheMeantime
AI Mastering (level 1.0, -8.0dB) 5 0.8969120467440757 InTheMeantime
LANDR (lo) 9 0.8764654694927303 InTheMeantime
LANDR (med) 4 0.9041434880571413 InTheMeantime
LANDR (hi) 12 0.8261471508284708 InTheMeantime

LeadMe

Service/Settings MEI20190207 Rank MEI20190207 Audio
Original 13 0.5779150707286589 LeadMe
AI Mastering (level 0.0, -12.0dB) 10 0.6426226859579769 LeadMe
AI Mastering (level 0.0, -10.0dB) 8 0.6817293955564483 LeadMe
AI Mastering (level 0.0, -8.0dB) 7 0.7022307718052216 LeadMe
AI Mastering (level 0.5, -12.0dB) 6 0.7625010222435649 LeadMe
AI Mastering (level 0.5, -10.0dB) 3 0.7839712178740927 LeadMe
AI Mastering (level 0.5, -8.0dB) 1 0.7999746597622968 LeadMe
AI Mastering (level 1.0, -12.0dB) 5 0.7633246115046106 LeadMe
AI Mastering (level 1.0, -10.0dB) 4 0.779679221827082 LeadMe
AI Mastering (level 1.0, -8.0dB) 2 0.793473257907946 LeadMe
LANDR (lo) 12 0.6104354748144973 LeadMe
LANDR (med) 11 0.6393640700499088 LeadMe
LANDR (hi) 9 0.6447399850652897 LeadMe

NotAlone

Service/Settings MEI20190207 Rank MEI20190207 Audio
Original 13 0.48257047219302485 NotAlone
AI Mastering (level 0.0, -12.0dB) 11 0.5231648622372371 NotAlone
AI Mastering (level 0.0, -10.0dB) 9 0.5553171592573396 NotAlone
AI Mastering (level 0.0, -8.0dB) 7 0.5939936969884216 NotAlone
AI Mastering (level 0.5, -12.0dB) 6 0.6231469488431141 NotAlone
AI Mastering (level 0.5, -10.0dB) 5 0.64725570362966 NotAlone
AI Mastering (level 0.5, -8.0dB) 4 0.681312613663448 NotAlone
AI Mastering (level 1.0, -12.0dB) 3 0.7092468467942112 NotAlone
AI Mastering (level 1.0, -10.0dB) 2 0.7391343588322199 NotAlone
AI Mastering (level 1.0, -8.0dB) 1 0.7738417297478148 NotAlone
LANDR (lo) 12 0.5177694307341181 NotAlone
LANDR (med) 10 0.5301580219081241 NotAlone
LANDR (hi) 8 0.5558005488267133 NotAlone

PouringRoom

Service/Settings MEI20190207 Rank MEI20190207 Audio
Original 10 0.30384273879121526 PouringRoom
AI Mastering (level 0.0, -12.0dB) 9 0.3336063781685228 PouringRoom
AI Mastering (level 0.0, -10.0dB) 8 0.3550314007243762 PouringRoom
AI Mastering (level 0.0, -8.0dB) 7 0.3579782008928494 PouringRoom
AI Mastering (level 0.5, -12.0dB) 6 0.4101967080751592 PouringRoom
AI Mastering (level 0.5, -10.0dB) 5 0.42280286809141887 PouringRoom
AI Mastering (level 0.5, -8.0dB) 4 0.4425067707057937 PouringRoom
AI Mastering (level 1.0, -12.0dB) 3 0.4870087573209856 PouringRoom
AI Mastering (level 1.0, -10.0dB) 2 0.49507863324789225 PouringRoom
AI Mastering (level 1.0, -8.0dB) 1 0.5144816971461217 PouringRoom
LANDR (lo) 13 0.28329351140384285 PouringRoom
LANDR (med) 12 0.2978973731809984 PouringRoom
LANDR (hi) 11 0.3007555113813294 PouringRoom

RedToBlue

Service/Settings MEI20190207 Rank MEI20190207 Audio
Original 13 0.4904558390370397 RedToBlue
AI Mastering (level 0.0, -12.0dB) 12 0.49955034816391186 RedToBlue
AI Mastering (level 0.0, -10.0dB) 10 0.537485544425039 RedToBlue
AI Mastering (level 0.0, -8.0dB) 7 0.5695406207973093 RedToBlue
AI Mastering (level 0.5, -12.0dB) 6 0.5824172293452627 RedToBlue
AI Mastering (level 0.5, -10.0dB) 5 0.6106067094000764 RedToBlue
AI Mastering (level 0.5, -8.0dB) 4 0.6359701881584552 RedToBlue
AI Mastering (level 1.0, -12.0dB) 3 0.6533688714703021 RedToBlue
AI Mastering (level 1.0, -10.0dB) 2 0.6720274923722775 RedToBlue
AI Mastering (level 1.0, -8.0dB) 1 0.6991642941950311 RedToBlue
LANDR (lo) 11 0.5339862191837641 RedToBlue
LANDR (med) 9 0.5447674263196023 RedToBlue
LANDR (hi) 8 0.5651982871738963 RedToBlue

Automated Mastering Settings

AI Mastering

Settings Name Target Loudness Automatic Mastering Mastering Level
Level 0.0, -12dB -12dB Disabled -
Level 0.0, -10dB -10dB Disabled -
Level 0.0, -8dB -8dB Disabled -
Level 0.5, -12dB -12dB Enabled 0.5
Level 0.5, -10dB -10dB Enabled 0.5
Level 0.5, -8dB -8dB Enabled 0.5
Level 1.0, -12dB -12dB Enabled 1.0
Level 1.0, -10dB -10dB Enabled 1.0
Level 1.0, -8dB -8dB Enabled 1.0

Common settings

  • Mode: Custom Mastering
  • Target Loudness Mode: Loudness
  • Ceiling Mode: Peak
  • Ceiling: -0.3dBFS (same as LANDR)
  • Oversampling: 2x
  • Automatic Mastering Preset: General
  • Specify Reference Audio By Myself: False
  • Sampling Rate: 44100Hz
  • Low Cut Freq: 20Hz
  • High Cut Freq: 20000Hz
  • Preserve Bass: True
  • Format: 16bit WAV

LANDR

Settings Name Intensity Level
Lo Lo
Med Med
Hi Hi

Common settings

  • Format: 16bit WAV

Test Audio Tracks

Test audio tracks are stored in audio/source/(song_name)/(mix_name)/(filename).mp3. Test audio tracks are chosen by following conditions.

Name Original Audio (44.1kHz 24bit formatted)
LeadMe LeadMe
InTheMeantime InTheMeantime
NotAlone NotAlone
PouringRoom PouringRoom
RedToBlue RedToBlue

Measures

MEI20190207

MEI20190207(MixEvaluationIndex20190207) is an index which is constructed based on Mix Evaluation Datasets. MEI20190207 is calculated by stepwise linear regression using AIC. Dependent variable is subjective mix evaluation score in Mix Evaluation Datasets.

Mix Evaluation Datasets: https://intelligentsoundengineering.wordpress.com/2017/09/01/the-mix-evaluation-dataset/

Selected explanatory variables and calculated coefs

variable coef
(Intercept) 1.03768547278472
bands_loudness0 0.00951325481004556
bands_loudness1 0.0164689071562976
bands_loudness7 0.0340216880860531
bands_loudness8 -0.0135561668568519
bands_loudness_range0 -0.0226949569352303
bands_loudness_range4 -0.0271575570115004
bands_mid_to_side_loudness2 0.0140066290330022
bands_mid_to_side_loudness5 -0.0104747618124023
covariance0_0 0.0919264284783258
covariance0_11 0.082558807133752
covariance0_12 -0.0342145316780618
covariance0_14 -0.0466198784131165
covariance0_15 0.0207276709645042
covariance0_16 0.0581059748423357
covariance0_17 -0.0393395203204817
covariance0_2 0.0411876866727139
covariance0_3 -0.0215521919930233
covariance0_9 -0.088577268407794
covariance10_13 -0.100081415393531
covariance11_12 0.108701115978759
covariance11_17 0.0379840675918427
covariance12_12 -0.0317636259791887
covariance1_12 -0.0589751072674823
covariance1_13 0.0284709024236286
covariance1_14 0.0556894684542315
covariance1_17 -0.0326130537643269
covariance1_4 0.0328017450878317
covariance2_10 0.0932133176211998
covariance2_16 -0.0682805249102863
covariance2_6 -0.0722886720490166
covariance3_11 -0.0356880644852162
covariance3_15 0.105958890021132
covariance3_3 -0.0585601702672593
covariance3_5 0.0414072559900464
covariance3_8 -0.0204928905344907
covariance4_15 0.0885603084432147
covariance4_6 0.0716787998562132
covariance5_15 -0.0808888836972396
covariance6_12 -0.0588472383077768
covariance6_7 -0.066752090504072
covariance7_8 0.155933392935423
covariance8_10 0.0368074690742597
covariance8_9 -0.0328605125125517
covariance9_15 -0.0519423428333282
covariance9_16 0.0655070769913487
dissonance -0.083778229293513
timbral_models_hardness 0.0303721487308621

Explanatory variable candidates

see columns of stats/audios.tsv

Learning Dataset

All preview data and subjective evaluation data that can be downloaded from http://c4dm.eecs.qmul.ac.uk/multitrack/MixEvaluation/. (some data are excluded because of 404)

Analyzer

AI Mastering Analyzer

An audio analyzer used in AI Mastering. This is not distributed.

Analysis data is in analysis directory Analysis data with pre loudness normalization is in analysis_normalized directory.

Pre loudness normalization is done because the learning dataset is loudness normalized. Pre loudness noramlization is done by adjusting ITU-R BS.1770 Loudness to -24.06.

The mean value of learning dataset ITU-R BS.1770 loudness is -24.06.

timbral_models

timbral_models is a python library to calculate some timbral indicies by AudioCommons. https://github.com/AudioCommons/timbral_models

timbral_models is used for calculate Hardness. Hardness is required to calculate MixEvaluationIndex20190207.

Pre loudness normalization is done like analysis_normalized.

Directories

audio

Source and mastered audio.

source -> formatted -> mastered

analysis

Analyzed data of all audios in audio directory.

stats

Statistics of analysis data.

FFmpeg

wav encode command

All source audios are formatted in 44.1kHz 24bit Float PCM WAV by following command to equalize experiment conditions.

ffmpeg -i /path/to/input.wav -ac 2 -ar 44100 -acodec pcm_s24le -f wav /path/to/output.wav

version

ffmpeg version 4.0.2 Copyright (c) 2000-2018 the FFmpeg developers
  built with Apple LLVM version 9.1.0 (clang-902.0.39.2)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/4.0.2 --enable-shared --enable-pthreads --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-gpl --enable-libmp3lame --enable-libx264 --enable-libxvid --enable-opencl --enable-videotoolbox --disable-lzma
  libavutil      56. 14.100 / 56. 14.100
  libavcodec     58. 18.100 / 58. 18.100
  libavformat    58. 12.100 / 58. 12.100
  libavdevice    58.  3.100 / 58.  3.100
  libavfilter     7. 16.100 /  7. 16.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  1.100 /  5.  1.100
  libswresample   3.  1.100 /  3.  1.100
  libpostproc    55.  1.100 / 55.  1.100

Related Work

THE MIX EVALUATION DATASET

Subjective mix evaluation data are collected. http://www.brechtdeman.com/publications/pdf/DAFx-17.pdf

Perceptual evaluation of music mixing practices

It reveals that the following 4 features are correlated with subjective mix evaluation score. https://www.researchgate.net/publication/283675867_Perceptual_evaluation_of_music_mixing_practices

LDR (Micro Dynamics)

Proposed in https://www.researchgate.net/publication/292846489_Measures_of_microdynamics. Algorithm summary is available in https://www.researchgate.net/publication/317091912_Towards_the_Development_of_Preference_Models_accounting_for_the_Impact_of_Music_Production_Techniques. The 95 percentile of diff between slow loudness(3sec) and fast loudness(25ms).

4 kHz octave band energy ratio (Spectral Envelope)

This feature seems to represent spectral envelope.

MFCC4 (Spectral Envelope)

This feature seems to represent spectral envelope.

side-to-mid energy ratio (Space)

This feature seems to represents space.

Modelling Timbral Hardness

Hardness index is constructed by using subjective evaluation data. https://www.mdpi.com/2076-3417/9/3/466 This hardness is implemented in https://github.com/AudioCommons/timbral_models.

Contact

If you have any questions please contact us. Questions about our service( AI Mastering ) are also welcome.

LICENSE

  • All files excluding audio directory are licensed under CC0.
  • Translation, writing about this survey, and quotation are welcome.
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