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deepdreameffect
====== Differences ====== This shows you the differences between two versions of the page.
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deepdreameffect [2015/10/25 11:07] dmr |
deepdreameffect [2015/11/05 04:57] (current) dmr |
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| | **Affiliation** | International Audio Laboratories Erlangen | | | **Affiliation** | International Audio Laboratories Erlangen | | ||
| | **eMail** | [[christian.dittmar@audiolabs-erlangen.de]] | | | **eMail** | [[christian.dittmar@audiolabs-erlangen.de]] | | ||
| + | | **code** | [[https://github.com/stefan-balke/hamr2015-deepdreameffect]] | | ||
| - | I propose to use Google's DeepDream processing as an audio effect. Therefore, I export music magnitude spectrogram as RGB channels of PNG images and apply to 'Gradient Ascent' with pre-trained networks to these images. | + | ===== What did I do ===== |
| - | {{ :overview.png?nolink&400 |}} | + | I used Google's DeepDream processing as an audio effect. Therefore, I export music magnitude spectrogram as RGB channels of PNG images and apply so-called 'Gradient Ascent' with pre-trained networks to these images. Afterwards, I convert the resulting images to spectrograms again and resynthesize them using Griffin and Lim's method. |
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| + | {{ :overview.png?nolink&800 |}} | ||
| Since the networks were trained on natural images, this makes no sense musically. However, it gives interesting results: | Since the networks were trained on natural images, this makes no sense musically. However, it gives interesting results: | ||
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| ===== Example 1: Piano ===== | ===== Example 1: Piano ===== | ||
| - | {{ :shenua.wav |}} | + | Input signal {{ :shenua.wav |}} |
| - | {{ :output_shenhua_layer3.wav |}} | + | Result using layer conv3 (MIT places network){{ :output_shenhua_layer3.wav |}} |
| - | {{ :output_shenhua_layer5.wav |}} | + | Result using layer pool5 (MIT places network){{ :output_shenhua_layer5.wav |}} |
| ===== Example 2: Ethno ===== | ===== Example 2: Ethno ===== | ||
| - | {{ :olcay.wav |}} | + | Input signal {{ :olcay.wav |}} |
| - | {{ :output_olcay_layer3.wav |}} | + | Result using layer conv3 (MIT places network) {{ :output_olcay_layer3.wav |}} |
| + | |||
| + | ===== Example 3: Breakbeat ===== | ||
| - | ===== Example 3: Separated Breakbeat ===== | + | Input signal (Different drums encoded as RGB) {{ :amenbrotherbreaknorm_mix.wav |}} |
| + | Result using layer conv3 (MIT places network) {{ :output_amen_layer3.wav |}} | ||
| - | {{ :amenbrotherbreaknorm_mix.wav |}} | + | ===== Libraries Used ===== |
| - | {{ :output_amen_layer3.wav |}} | + | |
| + | Anaconda Python Package | ||
| + | Caffe Deep Deep Learning Framework | ||
| + | Pre-Trained Networks | ||
| + | iPython Notebook | ||
| + | MATLAB | ||
deepdreameffect.1445785644.txt.gz · Last modified: 2015/10/25 11:07 by dmr
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