Neural Networks Weights and bias

Audio data conversion
weight or bias value

Some people grab audio input in one format (FLV let say) and then convert in into WAV to run against mature Sphix4 language model that developed for WAV format (Voxforge model for instance). It is strongly recommended to avoid such way of using. Conversion into WAV could produce malfunction of recognition engine.

At first stage of recognition Sphinx4 transforms audio data into feature vector and then into phonemes using acoustic model.

{DATA} -> [Sphinx4 Front End] -> {Feature Vector} -> {Phonemes}

Acoustic model is done using concrete file format and parameters such as sampling rate etc. It is recommended to put in recognitions engine original audio data of the same format.

Conversion of format, changing in sample rate can cause corruption of original data. Converted sound can be still good for human ears but can miss some important information for recognition.

Influence of conversion was studied an a few papers.

“A video stored on a media website such as YouTube has undergone a conversion from its original format to a compressed format, e.g. Flash Video (FLV). It is then retrieved for the experiment purpose and converted in suitable WAV format. However, vital information has been lost during the compression and quality of the final WAV audio is nearly equivalent to the compressed one. Besides, the input file should be of same format and quality as that with which the acoustic model was trained. Possibilities of getting interesting results using HUB4 to decode compressed news videos is thus low. A solution would be to train an acoustic model using FLV encoded samples.” (Boris Guenebaut ‘Automatic Subtitle Generation for Sound in Videos’)

Source: SmallSoftwareFactory - Java, J2EE development, speech and pattern recognition, neural networks

Neural Networks: A Tutorial
Book (Prentice Hall)

You might also like:

RCX Randomized Line Follower
RCX Randomized Line Follower

Basal metabolic rate

2012-01-24 22:58:21 by wounsel

So, on my century ride the other day, I ate a bunch of food to keep pedaling. I noticed that one of the guys on the ride who rode last time didn't seem to eat much. -- He's a vegan. When I asked him what he was going to eat for this ride he told me he had eaten cherries for breakfast, and he would eat some peanuts and some dried berries that he had in his jersey. WTF
What I'm wondering is how this guy can pedal a fixie 100 miles on solely nuts + berries + cherries + some water. It took me 2 oatmeal packs, 1 egg, on egg burrito from mcdonalds (a rest stop on the way), 4 clif bars, 2 granola bars, ~12 pieces of pizza @ cici's, salad & pasta @ cici's.... and when I got home, I was starving. -- one online calculator estimated about 3,900 calorie burn on top of whatever my basal...

Accounting Career

2007-09-05 15:50:59 by deefex007

Hey, i'm 25 and i'm giving up on the whole i wanna b a dj thing i was thinking, LOL. I am deciding i wanna grow up and stop partying and have a career. i know it's a bit late but whatever. I am thinking about going back to school and i think i'd like to major in accounting. now i'm not good with math, but i don't mind adding lots of number, using a calculator all day and doing paperwork. so i'm thinking of getting an associates and then seeing if i can get a job as a accounting clerk and then go to school for my bachelors while i continue working. which would be 2 more years for my bachelors right?. now does anyone think that this is a good idea? i know that not everyone who's in accounting do it because they love it. since i'm not that good at math, should i still do it? i know during...

Cavium Networks Stock Rating Reaffirmed by Jefferies Group (CAVM)  — Daily Political
Cavium Networks logo Cavium Networks (NASDAQ: CAVM)'s stock had its “buy” rating reiterated by investment analysts at Jefferies Group in a note issued to investors on Friday. They currently have a $42.00 target price on the stock, up from their ...

Related posts:

  1. Neural Networks Weight Decay
  2. Neural Networks Hebbian
  3. Neural Networks in Trading