5 Royalty-Free Audio Tracks for "Accuracy"

Sniper rifle shot sounds. Extracted from a video (http://img. Youtube. Com/vi/s0pcyw96ktw/2. Jpg) by a us government agency, thus public domain. Find many more military sounds in my military sounds pack, also located on freesound. Http://farm9. Staticflickr. Com/8070/8213683889_517a10ef52_o. Pngon flac and ogg vorbis audio file formats. Contact me if you have interest in specific sounds for open source or commercial purpose.

Doing a few typing speed tests on my 5-year-old dell laptop. Average wpm around 70, average accuracy around 96%. Recorded on tc tonor microphone.

A scoff before telling an opponent to perish. In my normal pitch range. (note: i do not speak japanese so i cannot confirm the accuracy of this sound).

This sound is a 1-sample long impulse at 48 khz. It covers the whole frequency range with equal power. This is a perfect sample for exciting your guitar amp or reverb unit to capture it's impulse response (ir). You can also play it through a speaker in a reverbant room to capture it's reverb characteristics. Remember that the ir sample will be no flatter than your speaker's performance multiplied by your microphone's performance (frequency response characteristics). The sample has exaclty 1 second of silence, then the impulse, then another second of silence to ensure the impulse will be played clean and untruncated on any sound system or device. My test with ir lv2 convolution plugin have proven, that this sample has absolutely flat frequency response - convolved signal was identical to the source signal. After normalization and sample-alignment of the sound clips i have inverted the polarisatin of one of them and summed them - result was absolute silence, even no hiss was present as a result. This shows the accuracy of the convolution process and proves this sound to be perfect for sampling ir. The impusle was generated with c* dirac ladspa plugin. Created using audacity.

We develop iphone app that perform musical analysis on recorded audio from the iphone. Our app implementation make use of the audio queue service to receive raw audio buffers from the audio queue callback. In the first version of our app we had the problem of too much clipping on the recording which degrade the accuracy of our analysis. We also suspected that the noise canceling algorithm in iphone 5 produce distorted sound, which is not much noticeable by human ear but distorted enough to affect our sensitive algorithm. We found that the solution to our problem is to set the audio session mode to kaudiosessionmode_measurement. This session mode is supposed to give maximum freedom for us to control the microphone input, which include turning off the automatic gain control and probably noise canceling as well. The solution works very well except that it introduce a strange waveform pattern in the beginning of all recordings in iphone 5. It is very hard to explain the waveform we get, so i made two recordings at freesound so that you can see it visually. The first recording is made in an almost quite environment, and you can see the weird spike in the beginning of the recording. The second recording (this recording) is made with constant background noise, and you can see that the actual sound wave is offset from the strange curve and gradually increase to its original volume. This waveform only happens on iphone 5 devices that we tested, and there is no problem at all for iphone 4s and older generations. We have tried various settings and the glitch is still unavoidable as long as we set the audio session mode to kaudiosessionmode_measurement. We also find similar glitch in one of our iphone 5 devices, in which the glitch happens even if we try to set just the input gain level without changing the session mode. We are not sure if this is a hardware-related bug in iphone 5, or if it is fixable software glitch in the future version of ios. For the moment we are looking for workaround that can avoid this glitch while automatic gain control and noise canceling are disabled.