A toolbox for animal call recognition

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems

Statistical analysis of the features of diatonic music with jMusic

Much has been written about the rules of melody writing and this paper reports research that uses computer-based statistical analysis to test the efficacy of these rules. As a method to assist in the computer generation of melodies, we have devised computer software that analyses melodic features. This paper will outline the melodic features identified in melody-writing literature and the results of their fit with our statistical analysis of melodies from the western music repertoire. We will also present details of the computer-based analysis software and the jMusic software environment in which it was built. The software and jMusic environment are open source software projects that are freely available, and so opportunities to develop these tools to suit other music analysis tasks will be discussed.Hosted by the Scholarly Text and Imaging Service (SETIS), the University of Sydney Library, and the Research Institute for Humanities and Social Sciences (RIHSS), the University of Sydney

Using a novel visualization tool for rapid survey of long-duration acoustic recordings for ecological studies of frog chorusing

Continuous recording of environmental sounds could allow long-term monitoring of vocal wildlife, and scaling of ecological studies to large temporal and spatial scales. However, such opportunities are currently limited by constraints in the analysis of large acoustic data sets. Computational methods and automation of call detection require specialist expertise and are time consuming to develop, therefore most biological researchers continue to use manual listening and inspection of spectrograms to analyze their sound recordings. False-color spectrograms were recently developed as a tool to allow visualization of long-duration sound recordings, intending to aid ecologists in navigating their audio data and detecting species of interest. This paper explores the efficacy of using this visualization method to identify multiple frog species in a large set of continuous sound recordings and gather data on the chorusing activity of the frog community. We found that, after a phase of training of the observer, frog choruses could be visually identified to species with high accuracy. We present a method to analyze such data, including a simple R routine to interactively select short segments on the false-color spectrogram for rapid manual checking of visually identified sounds. We propose these methods could fruitfully be applied to large acoustic data sets to analyze calling patterns in other chorusing species

Content Description of Very-long-duration Recordings of the Environment

Long-duration sound recordings are an established technique to monitor terrestrial ecosystems. Acoustic sensing has several advantages over personal field-surveys, but a disadvantage is that technological advances enable collection of much more audio than can be listened to. Machine learning methods can identify individual species, but these are time-consuming to build and if the species of interest is absent, nothing is revealed about recording content. Visual methods have also been developed to interrogate long-duration recordings but ultimately, interpretation of acoustic recordings must be ground-truthed by listening to the actual sound. However, the ear is constrained to listen in real-time. Even if one listens to 10 hours of one-minute segments, selected randomly from one year of recording, this represents only a 0.11% sample of the data. For this study, we recorded 13 months of continuous audio in natural Australian woodland. We divided the audio into one-minute segments, which yields a content description at one-minute resolution. The feature set representing each segment consists of summary and/or spectral acoustic indices. Our objective in this investigation is two-fold: (1) to maximise content description of a very-long-duration recording while keeping listening to manageable levels; and (2) to determine how content description is influenced by the choice of acoustic features and other variables. We begin by clustering the acoustic feature vectors using the k-means algorithm. Given sufficient clusters (k = 60), each cluster can be interpreted as a discrete acoustic state within the year-long soundscape. We describe four findings: 1. Listening to the medoid minute of each cluster (the minute whose feature vector is closest to the cluster centroid) yields a similar content description to that obtained by listening to a random sample of ten minutes from each cluster. This represents a ten-fold reduction in listening effort. 2. Although k-means is known to produce different clustering outcomes depending on cluster initialisation, we find that content description is little affected by different runs of k-means. 3. Different feature vectors yield a slightly different content description depending on which acoustic events have been ‘targeted’ by the selected features. 4. Training a Hidden Markov Model on the year-long cluster sequence helps to identify the underlying acoustic communities and can be used to obtain a more fine-grained labelling of sound-sources of interest

An algorithm to cluster the spectra in a spectrogram

The work described in this technical report is part of an ongoing project at QUT to build practical tools for the manipulation, analysis and visualisation of recordings of the natural environment. This report describes the algorithm we use to cluster the spectra in a spectrogram. The report begins with a brief description of the signal processing that prepares the spectrograms

C-Net: A Method For Generating Non-Deterministic And Dynamic Multi-Variate Decision Trees

Despite the fact that artificial neural networks (ANNs) are universal function approximators, their black box nature (that is, their lack of direct interpretability or expressive power) limits their utility. In contrast, univariate decision trees (UDTs) have expressive power, usually though they are not as accurate as ANNs. We propose an improvement, C-Net, for both the expressiveness of ANNs and the accuracy of UDTs by consolidating both technologies for generating multivariate decision trees (MDTs). In addition, we introduce a new concept, recurrent decision trees, where C-Net uses recurrent neural networks to generate an MDT with a recurrent feature. That is, a memory is associated with each node in the tree with a recursive condition which replaces the conventional linear one. Furthermore, we show empirically that, in our test cases, our proposed method achieves a balance of comprehensibility and accuracy intermediate between ANNs and UDTs. MDTs are found to be intermediate since they are more expressive than ANNs and, more accurate than UDTs. Moreover, in all cases MDTs are more compact (i.e. smaller tree size) than UDTs

Bacterial Promoter Modelling and Prediction for E.coli and B.subtilis with Beagle

We constructed sigma70-promoter models of varying complexity to predict promoter locations and to evaluate the importance of specific promoter elements. For this purpose, a novel software, named Beagle, was developed that utilizes an easy description language to conveniently specify promoter models. Model specifications are translated into position weight matrices and gap distributions which are refined using data from known promoters. The method is transparent, fast and allows the rapid exploration of different promoter models. Applied to promoter prediction in E. coli and B. subtilis, we show that inclusion of UP-elements and extended -10 motifs into the model yields a significant increase in prediction accuracy. The software, data sets and extended results can be downloaded at http://eresearch.fit.qut.edu.au/Beagle/

BLOMAP: An Encoding Of Amino Acids Which Improves Signal Peptide Cleavage Site Prediction

Research on cleavage site prediction for signal peptides has focused mainly on the application of different classification algorithms to achieve improved prediction accuracies. This paper addresses the fundamental issue of amino acid encoding to present amino acid sequences in the most beneficial way for machine learning algorithms. A comparison of several standard encoding methods shows, that for cleavage site prediction the frequently used orthonormal encoding is inferior compared to other methods. The best results are achieved with a new encoding method named BLOMAP – based on the BLOSUM62 substitution matrix – using a Naïve Bayes classifier

The In Silico Prediction of Promoters in Bacterial Genomes

In silico approaches to the identification of bacterial promoters are hampered by poor conservation of their characteristic binding sites. This suggests that the usual position weight matrix models of bacterial promoters are incomplete. A number of methods have been used to overcome this inadequacy, one of which is to incorporate structural properties of DNA. In this paper we describe an extension of the promoter description to include SIDD (stress induced duplex destabilization), DNA curvature and stacking energy. Although we report the best result to date for a realistic promoter prediction task, surprisingly, DNA structural properties did not contribute significantly to this result. We also demonstrate for the first time, that sigma-54 promoters have a stronger association with SIDD than do other promoter types

Statistical Analysis of the features of diatonic music using jMusic software

The use of computers for analysis of diatonic melodies can be useful in the identification of interesting features often unobservable with manual analysis and provides a vehicle for the comparative analysis of individual melodies or classes of melodies. This paper presents our work in melodic feature analysis based on simple rules of diatonic melody writing. Through the testing of these features against a data set of melodies from Western music history we show which features are closely or loosely adhered to by composers in practice. We also show how individual melodies can be compared against the norms to highlight interesting characteristics for further manual analysis. Our music analysis software described in this paper makes the task of feature analysis relatively effortless, and its graphical presentation of results enables efficient and multi-modal communication of the data. We outline the basic operation of this software and provide details enabling others to access and perhaps modify the software for their needs. For example, one area of extension would be the provision of correlation between features. The computer has proved to be useful tool in focussing our thinking about diatonic music (in particular melodic construction), assisting with the analysis of large data sets, and in clarifying heuristics for algorithmic computational music creation