Egardless of whether series p and q correspond to successive positions in time, or in any other dimension.Note that, contrary to DTW, GMMs reduces a series of observations to a single random variable, i.e discard order details all random permutations with the series along its ordering dimension will result in precisely the same model, even though it won’t with DTW distances.We nonetheless contemplate unordered GMMs as a “series” model, simply because they impose a dimension along which vectors are sampled they model information as a collection of observations along time, frequency, price or scale, along with the decision of this observation dimension strongly constrains the geometry of info readily available to subsequent processing stages.The decision to view information either as a single point or as a series is occasionally dictated by the physical dimensions preserved in the STRF representation soon after dimensionality reduction.If the time dimension is preserved, then data can’t be viewed as a single point simply because its dimensionality would then vary together with the duration of the audio signal PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/21515227 and we would not be able to evaluate sounds to one particular a different within the identical feature space; it could only be BGT226 PI3K processed as a timeseries, taking its values inside a constantdimension function space.For the identical reason, series sampled in frequency, rate or scale cannot take their values in a feature space that incorporates time.Precisely the same constraint operates around the combination of dimensions which might be submitted to PCA PCA can not cut down a feature space that incorporates time, simply because its dimensionality would not be constant.PCA might be applied, having said that, around the constantdimension feature.Case Study Ten Categories of Environmental Sound TexturesWe present here an application from the methodology to a modest dataset of environmental sounds.We compute precision values for different algorithmic methods to compute acoustic dissimilarities among pairs of sounds of this dataset.We then analyse the set of precision scores of these algorithms to examine whether certain combinations of dimensions and specific solutions to treat such dimensions are a lot more computationally powerful than other folks.We show that, even for this little dataset, this methodology is capable to identify patterns which might be relevant both to computational audio pattern recognition and to biological auditory systems..Corpus and MethodsOne hundred s audio files had been extracted from field recordings contributions on the Freesound archive (freesound.org).For evaluation purpose, the dataset was organized into categories of environmental sounds (birds, bubbles, city at night, clapping door, harbor soundscape, inflight data, pebble, pouring water, waterways, waves), with sounds in each category.File formats had been standardized to mono, .kHz, bit, uncompressed, and RMS normalized.The dataset is obtainable as an net archivearchive.orgdetails OneHundredWays.On this dataset, we evaluate the efficiency of specifically different algorithmic approaches to compute acoustic dissimilarities among pairs of audio signals.All these algorithms are depending on combinaisons with the four T, F, R, S dimensions of the STRF representation.To describe these combinations, we adopt the notation XA,B…for any computational model depending on a series within the dimension of X, taking its values inside a function spaceFrontiers in Computational Neuroscience www.frontiersin.orgJuly Volume ArticleHemery and AucouturierOne hundred waysconsisting of dimensions A,B…For instance, a time series of frequency values is written as TF and time se.
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