Beehive Audio Source Separation Dakota Murray

What is Audio? Probably zoom in on the frequencies less than 2500 Hz. Just a bunch of numbers! Time domain Frequency Domain Spectrogram Probably zoom in on the frequencies less than 2500 Hz.

The Spectrogram Each column is a frequency spectrum at a point in time Discretized. Tradeoff between number of time frames, and number of frequency bins Discretized? Tradeoff between time resolution and frequency resolution.

Signal Separation Separate sources from a mixture Paris Smaragdis Signal Processing researcher Approached signal separation probabilistically He models each source as a mixture of distributions over the frequency spectrum. The resulting spectrogram is a mixture of these sources and noise.

Smaragdis’s Approach 1 A source can be represented as a stochastic “picker” randomly selecting from a set of jars Each jar is a component of the source and each has different distributions of marbles inside A frequency is written on each marble Given a component z, what is the probability of the picker pulling frequency f

Smaragdis’s Approach 2 Mixtures of sources add another variable, s, to the model. If there are two sources, our picker is now selecting from two sets of jars, each set with their own distributions Given a source s, and component z, what is the probability of the picker pulling frequency f

Original Signals Voice Chimes Separated Signals

Training Target   x

Training Interference   x

Step 2: Separation Decompose the mixture of our known sources in terms of the contributions of the basis vectors for each source In other words, find out how each source contributes to the mixture.

Step 3: Reconstruction * = * =

Variation on the Process Supervised We have an example of target and interference Semi-Supervised We only know the target. Interference could be anything Train the target as normal. Learn Interference from our mixture. Interference represents everything that isnt the target. Fixed Target: Target Vectors can not change Target As Prior: Target Vectors are allowed to change

The Importance of Honey Bees Honeybees are dying from Colony Collapse Disorder Honeybee pollination results in ⅓ of the food humans consume. An agricultural, ecological, and economic issue. Data is useful. Good data is scarce. Often full of interference from traffic, weather, and electronics Cite all images and equations from somewhere else.

Bee Hive Audio Considerations Can’t be certain what type of interference to expect. Should use a Semi-Supervised method of training the interference basis vectors Don’t want to assume we know exactly what the bees will sound like. Use a Target as Prior method of training the interference, thus allowing the target to change. Might be getting a lot of data. Must optimize parameters for best performance to execution time ratio Testing should involve more real world scenarios

Problems To get good results from a complicated signal, the algorithm is time consuming The training process only find local minimum, may not produce idea set of basis vectors. Has difficulty separating sources with occupy similar frequency ranges.

Direction for Future Testing, testing, testing, and more testing (optimizing parameters, trying out different testing scenarios) Gathering more bee hive data. App State students currently working on Low Cost hive monitoring hardware More real-world scenarios Work with other types of data, not just bees.

Acknowledgements Paris Smaragdis for paving the way Dr. Parry for his mentoring, assistance, and wealth of knowledge Dr. T and the Rest of the App State Department of Computer Science for providing the encouragement and opportunity NC State for hosting this event!

References Bhiksha Raj and Paris Smaragdis, "Latent variable decomposition of spectrograms for single channel speaker separation," in Proc. of IEEE Workshop on Applications of Signal Proc. to Audio and Acoustics, New Paltz, USA, 2005. P. Smaragdis , B. Raj and M. V. Shashanka "Supervised and semi-supervised separation of sounds from single-channel mixtures", Proc. 7th Int. Conf. Ind. Compon. Anal. Signal Separation (ICA\'07), pp.414 -421 2007 M. V. S. Shashanka , B. Raj and P. Smaragdis "Sparse overcomplete decomposition for single channel speaker separation", Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP\'07), vol. 2, pp.II-641 -II-644 2007