The iPhone version of AI Mastering has been updated to v1.2.5.
・ We speed up video upload and reduced transfer volume .
It is now possible to master videos encoded with H.264 and H.265 without re-encoding the video part .
In the past, all videos, including video, were uploaded to the server, and the server generated output videos. Therefore, when the capacity of the video part was large, upload and download took time . Also, in order to reduce the transfer volume, the smartphone sometimes re-encoded before uploading. Re-encoding took time and the image quality deteriorated .
In the modified version, in the case of an MP4 movie made with H.264, H.265 + AAC, the video and sound are Demuxed (separated without degradation) on the smartphone side, and only the sound is uploaded and mastered. When mastering is over, download the sound after mastering, Mux the sound and video on the smartphone side and combine them with no degradation to create an output video.
For all other videos, upload the entire video as usual and make the output video on the server side. In this case, the video is re-encoded to H.264 + AAC MP4.
As a result, speeding up, high image quality, and transfer volume reduction were realized.
※ In the case of MP4 movie made with H.264, H.265 + AAC, the video part can not be seen when the one uploaded in the smartphone version is viewed in the PC version or in other smartphones. Please note.
iPhone version update method
It is possible to update from the following App Store link.
If you have any comments or requests regarding the smartphone version, I would be happy if you can tell us.
Please wait for a while as Android is being supported.
4. Japanese localization with Equalizer APO Translator
Launch Equalizer APO Translator and press the "Apply Japanese Localization" button to perform Japanese localization. When finished, it is OK to exit Equalizer APO Translator.
5. Start Equalizer APO Configuration Editor
Launch Equalizer APO Configuration Editor.
6. Set the language setting of Equalizer APO Configuration Editor to English
Set the language setting of the Equalizer APO Configuration Editor to English. It is difficult to understand, but if you set the language setting to English, it will be Japanese.
When Japanese localization is completed, it will be as follows.
I want to translate into other languages
Languages supported by the official Equalizer APO are English and German. If you want to translate Equalizer APO into languages other than English, German or Japanese, you may be able to respond by sending a translation of the translation part of the following file into the target language. However, please note that there is no guarantee that it can be supported.
It is the result of analyzing "be yourself" of "DÉ DÉ MOUSE" by AI Mastering.
Analysis result of “DD DÉ MOUSE” and “be yourself” by AI Mastering
Loudness time series
How can I get the same sound pressure as "DÉ DÉ MOUSE" and "be yourself"?
According to the analysis results, the loudness of "be yourself" is -7.6 dB, so I think that it is better to set to a target sound pressure a little larger than that in AI Mastering and mastering.
Because the target sound pressure is high, the Ceiling setting is recommended "Peak" or "True Peak". "True Peak (15 kHz Lowpass)" is too conservative for clipping because it lowers the peak so that it does not clip if it is cut more than 15 kHz due to re-encoding, etc.
If you set "True Peak", it is recommended to set oversampling to 2x.
AI Mastering has been updated. A new mastering algorithm "v2" has been added.
New algorithm "v2"
Added new mastering algorithm "v2" to custom mastering. You can select the new algorithm "v2" and the conventional algorithm "v1" in the advanced option.
* "V2" is selected for One Touch Mastering (Easy Mastering).
Features of the new algorithm "v2"
v2 masters so that "Proposity 2" goes up. Since the search performance of the mastering parameter is higher than v1 , "Prop2" rises with high probability.
It is also possible to specify a reference. When a reference is specified, mastering is made so as to approach the reference, not to raise "Prop. 2". You can not specify a preset like v1.
It includes processing to minimize the departure from the original sound quality, whether you specify a reference or not. In v1 there was a case that the sound quality changed extremely depending on the sound source, but it is relaxed.
The mastering level allows you to adjust how far away you want from the original sound quality.
Characteristics of conventional algorithm "v1"
v1 masters so that "Proposity" goes up. As we select the mastering parameters heuristically, the "pros" may not improve much.
Changing the mastering parameters of AI Mastering changes "Proposity" in various ways, but adjusting it to increase "Proposity" makes the sound better. Can you automate this? I received an opinion.
It is v2 that corresponds to this opinion. Since the search algorithm for mastering parameters is enhanced in v2, mastering is done automatically so that "Prop. 2" becomes large. I think that it will be less time to manually adjust the parameters.
Professionalism 2 (Professionality 2)
"Proposity 2" is an index that improves "Proposity". Added to analytical indicators. We are learning with more data than "Proposity".
Increased upload size upper limit and source length upper limit for PC version
New upload size limit: 250MB (conventional: 150MB)
New sound source upper limit: 15 minutes (conventional: 10 minutes)
Please wait for a while as the iPhone version supports the application side.
Enhanced monitoring to prevent system failures due to unknown causes.
Premium Plan Double Billing
Some people registered for the premium plan were charged twice.
Specifically, if payment for payment fails for some reason while registering for the premium plan, then register for the premium plan again, and then if the payment for the old premium plan is restored, the old premium plan and the new premium plan will be simultaneously Continued and charges were double charged.
We will do the following for those who are eligible. There is no need for customer support. I was very sorry.
A: Canceling the old premium plan
B: Double charge refund
C: In addition to B, as an apology I refunded the most recent one month's worth
Also, I have fixed the program to prevent it from happening again. In the future, we have enhanced monitoring so that we can discover early even if double charges occur due to unknown cases.
We compared LANDR and AI Mastering in sound quality.
We proposed an index that can objectively evaluate the mix MEI 20190207.
We compared the sounds mastering with AI Mastering and LANDR at MEI 20190207.
We found that AI Mastering has higher MEI 20190207 than LANDR.
AI Mastering has a tendency that the loudness range is larger than the LANDR, the Boominess is small, the Depth is small, and the Warmth is small.
* Since there are comparative sounds in the other people, please listen
Mastering various sounds with LANDR and AI Mastering and comparing the results with various indicators.
Sound to be compared
We chose the sound to be compared from the following mix evaluation data set. This mix evaluation data set includes multiple mixed audio for various songs and subjective evaluation results by multiple people for each mixed audio.
In the mix audio license, CC BY's, we selected the one with the largest loudness range for each song and the one with the lowest average subjective rating as the comparison target tone.
The reason is that it is easy to master without artifacts when the loudness range is large, and there is a mismatch of automatic mastering when the subjective evaluation is low.
Please see the GitHub repository below for a specific mix list.
MixEvaluationIndex 20190207 (MEI 20190207) is an objective evaluation index of mixed audio constructed using subjective evaluation data of The Mix Evaluation Dataset. It is an evaluation index of mixed audio, but I think that it can also be used for evaluating mastering audio. It is intended for comprehensive evaluation. It is the main indicator in this comparison.
MEI 20190207 is calculated by the weighted sum of various indices. The original indices are the spread covariance matrix of the spectrum, the mean of the spectrum, Hardness, Dissonance. Simply put, I calculate it based on the shape of the spectrum, the dynamic range, the spread of space, the bandwidth of the attack, and the amount of distortion.
The mixed audio used for weight learning is all the mixed audio that is published in MixBrowser, with preview audio. Some preview audio was 404 Not Found.
It is the loudness defined by ITU-R BS.1770. Depending on the platform to be delivered and how the user listens, it is highly likely that songs with loudness are more likely to be played with louder sounds as compared to other songs. It sounds better as you play with loud sounds.
If the sound quality is the same, the loudness should be large.
Loudness range, True Peak
Please see GitHub below.
Change amount of MEI 20190207
It is the average of MEI 20190207 change with respect to the original for all songs. AI Mastering tends to have higher MEI 20190207 than LANDR.
Loudness vs. Loudness range
I plotted the average of all songs in loudness and the average of all songs in loudness with a scatter plot. In general, the loudness and the loudness range are in a trade-off relationship, but the decrease in the loudness range when AI Mastering has a higher loudness than LANDR is small.
True Peak (inter sample peak) is an average of all songs. If True Peak is larger than 0 dB, it may be distorted due to re-encoding etc, but there seem to be cases where both AI Mastering and LANDR exceed 0 dB. If you set Ceiling to True Peak in AI Mastering, you can prevent True Peak from exceeding 0 dB, so you can avoid degrading sound quality. LANDR is probably impossible to avoid because there is no such setting.
Change amount of Dissonance
Dissonance is an index to measure dissonance degree. It is used to calculate MEI 20190207, the lower the Dissonance, the higher the MEI 20190207.
If you set the mastering level to 1 in AI Mastering, Dissonance seems to increase. Setting the mastering level to 0.5 will result in an increase equivalent to LANDR.
Change amount of Hardness
Hardness is an index to measure hardness of sound. It is used to calculate MEI 20190207, and the higher the Hardness is, the higher the MEI 20190207 is. Both AI Mastering and LANDR seem to increase Hardness.
Changes in Boominess
Boominess is an index for Boomy degree. The contents are Booming Index proposed below. It is not used for calculation of MEI 20190207.
Brightness is an indicator of brightness. It is calculated by the linear combination of the logarithm of the energy ratio of the high frequency component to the total energy and the logarithm of Spectral Centroid. It is not used for calculation of MEI 20190207.
Depth is an indicator of depth. It is defined in D 5.2 below. According to D 5.2, the depth has spatial meaning and frequency characteristic meaning, but this Depth index represents only frequency characteristic meaning. It is not used for calculation of MEI 20190207.
For each song, I picked up three of the original, LANDR with the largest MEI 20190207, AI Mastering with MEI 20190207 the biggest one. Since loudness is not aligned, please be careful of the bias due to the difference in volume.
All sound lists are below. Please try MEI 20190207 whether the high sound is really good sound. The license notation of each song is described under the audio directory of Github.