Yong's Experiments

-
A previous employee of CVSSP that I never met, Dr. Yong Xu, was working a bit on soundscape synthesis using SampleRNN. For posterity, here are the contents of a Power Point that I obtained from him.


More audio generation demos for different acoustic scene classes

Demo



Generated restaurant/cafe audio

Conclusions:
  1. i-vector is more stable than the one-hot vector
  2. the quality of the generated audio is better



Generated beach audio

Successfully generated the audio!!!



Generated park audio
Some bird song is generated

Successfully generated the audio!!!



Generated cafe/restaurant audio
Some human talking bubble sound and glass colliding is generated

Successfully generated the audio!!!

Compared with the piano/speech generation using sampleRNN:
Audio is more difficult to generate, negative log-likelihood: 2.8 for audio VS 1.0 for piano VS 1.0 for speech

Generated piano

Successfully generated the audio!!!



Unfortunately I don't have much additional contextual information about how these were made. I know that he was working with this implementation, and was using the DCASE 2016 task 1 dataset. I also know that he was working to modify SampleRNN to accommodate different categories of soundscape via global conditioning, but a former colleague of his told me that these were generated by training individual models each accommodating only one type of soundscape.

Comments

Post a Comment

Popular posts from this blog

Ambisonic Rendering in the Story Bubble

-
Image
Figure 1: Sound Sphere in the Center for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey. The person is shown standing in the intended orientation, facing the front of the sphere. Introduction I recently needed some audio content that I could quickly render for playback in the sound sphere shewn in Figure 1. There are a reasonable amount of free ambisonic recordings available online, e.g. Angelo Farina Youtube So I spent some time putting together tools to render ambisonics in the sphere, and here I wanted to share what I learned in case anybody else at CVSSP wants to try this out in the future. Speaker Arrangement The sphere has 24 speakers (plus a subwoofer which I will henceforth ignore), which at the time of writing are arranged as shown in Figure 2 and Figure 3. Figure 2: Speaker layout. The center of the diagram represents the top of the sphere. The top of the diagram represents the front of the sphere. There is one spe
Read more

How I calibrated my contact microphone

-
Image
Introduction I used the Hsu-Nielson method to find the frequency response of my contact microphones. This method involves cracking a mechanical pencil graphite on the microphone transducer. The microphone in question is the one shown above, and described in detail here: http://michaelkrzyzaniak.com/marshmallow.php I specifically used the white, low-gain variant. Experimental Setup and Results Figure 1: The mechanical pencil used in this experiment, with 0.5mm number-2 graphite Figure 2: Three millimeters of graphite were extended from the pencil Figure 3: Audio interface used for experiment (Scarlett 2i4), showing the settings used. Figure 4: Video showing the graphite being cracked. A small amount of putty was used to prevent the tip of the pencil tapping the transducer after the graphite breaks. The graphite is placed on the transducer and constant pressure is applied. When the graphite breaks, the pressure is released in a very short time (shorter than th
Read more

WaveRNN

-
Image
Figure 1: CVSSP's Pet Wyvern. Introduction I've been thinking about how we can make audio synthesis faster. In particular, I would be interested in realtime soundscape synthesis, partially because I think it would be good for the project, and partly because it would be well aligned with my own personal research goals. I found two options: Parallel Wavenet For a while, I was looking at parallel wavenet . This can supposedly generate samples in realtime, and is now used in the Google Assistant. However, I have been unable to find a good, vanilla implementation of it, and the paper is really sparse on details. There are some projects on GitHub where people have started implementing it more than 6 months ago and have not finished, so given the time constraints of this project, implementing it myself doesn't seem feasible. Moreover, the training process is very intricate and involves training a couple of networks separately and then together -- which makes it really har
Read more