Counting livestock is generally done only during major events, such as drenching, shearing or loading, and thus farmers get stock numbers sporadically throughout the year. More accurate and timely stock information would enable farmers to manage their herds better. Additionally, prompt response to any stock in distress is extremely valuable, both in terms of animal welfare and the avoidance of financial loss. In this regard, the evolution of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) is forging a new research area for remote monitoring and counting of different animal species under various climatic conditions. In this paper, we focus on detecting and counting sheep in a paddock from UAV video. Sheep are counted using a model based on Region-based Convolutional Neural Networks and the results are then compared with other techniques to evaluate their performance.

Recently, wireless acoustic sensor networks (WASNs) have received significant attention from the research community and a variety of methods have been proposed for numerous applications, such as location estimation and speech enhancement. The lack of publicly available datasets with signals recorded in WASNs, presents difficulties in obtaining consistent performance indicators across the different approaches. In this paper, we present and release a dataset of real recorded signals in an outdoor WASN comprised of four microphone arrays. Our dataset consists of several speakers recorded at various locations within the WASN and can be used for benchmarking purposes. We also present location estimation results using our real recorded dataset. Our results can serve as a baseline indicator of localization performance of single and multiple sources in a real environment.

Significant improvements in intelligibility of speech in noise can be obtained by modifying the speech signal in the time and/or frequency domains. However, most speech intelligibility enhancement algorithms are designed to use clean speech as an input, and their performance suffers once the input speech signal-to-noise ratio decreases, a common case in face-to-face communication environments such as restaurants or cafés. In this work we investigate whether a particularly successful speech intelligibility enhancement system-spectral shaping and dynamic range compression-and various front-end noise reduction methods might be suitable in such environments. Our evaluations suggest that such a complete system would provide an increase in speech intelligibility equivalent to a gain of 10 dB input signal-to-noise ratio in the more challenging face-to-face communication environments.

We propose a real-time method for coding an acoustic environment based on estimating the Direction-of-Arrival (DOA) and reproducing it using an arbitrary loudspeaker configuration or headphones. We encode the sound field with the use of one audio signal and side-information. The audio signal can be further encoded with an MP3 encoder to reduce the bitrate. We investigate how such coding can affect the spatial impression and sound quality of spatial audio reproduction. Also, we propose a lossless efficient compression scheme for the side-information. Our method is compared with other recently proposed microphone array based methods for directional coding. Listening tests confirm the effectiveness of our method in achieving excellent reconstruction of the sound field while maintaining the sound quality at high levels.

In this work, a multiple sound source localization and counting method is presented, that imposes relaxed sparsity constraints on the source signals. A uniform circular microphone array is used to overcome the ambiguities of linear arrays, however the underlying concepts (sparse component analysis and matching pursuit-based operation on the histogram of estimates) are applicable to any microphone array topology. Our method is based on detecting time-frequency (TF) zones where one source is dominant over the others. Using appropriately selected TF components in these “single-source” zones, the proposed method jointly estimates the number of active sources and their corresponding directions of arrival (DOAs) by applying a matching pursuit-based approach to the histogram of DOA estimates. The method is shown to have excellent performance for DOA estimation and source counting, and to be highly suitable for real-time applications due to its low complexity. Through simulations (in various signal-to-noise ratio conditions and reverberant environments) and real environment experiments, we indicate that our method outperforms other state-of-the-art DOA and source counting methods in terms of accuracy, while being significantly more efficient in terms of computational complexity.

We propose a novel real-time adaptative localization approach for multiple sources using a circular array, in order to suppress the localization ambiguities faced with linear arrays, and assuming a weak sound source sparsity which is derived from blind source separation methods. Our proposed method performs very well both in simulations and in real conditions at 50% real-time.

Research in online shopping was the focus of many studies as the age of electronic commerce began in the late 1990's. More recently, research in this area has declined, even as shopping on the Internet continues to increase and now dominates some product categories. This research offers a timely update on this literature by investigating online shopping from a perceived risk perspective. The results find that overall perceived risk is low with only some consumer concerns in psychological, time and performance risk. Analysis of perceived risk across six product categories and four demographic factors finds a significant level of perceived risk for lower income individuals when purchasing consumer electronics, but not in any other construct examined in this research. Overall, this study provides empirical evidence to substantiate the common perception that perceived risk in online shopping is declining and does not differ greatly across product category or demographic factor.

Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-of-the-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.

In this paper, the compressed sensing (CS) methodology is applied to the harmonic part of sinusoidally-modeled audio signals. As this part of the model is sparse by definition in the frequency domain, we investigate how CS can be used to encode this signal at low bitrates, instead of encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do. We extend our previous work by considering an improved system model, by comparing our model to other schemes, and exploring the effect of incorrectly reconstructed frames. We show that encouraging results can be obtained by our approach, although inferior at this point compared to state-of-the-art. Good performance is obtained using 24 bits per sinusoid as indicated by our listening tests.

A model is developed for finding codes for differentially demodulated continuous phase frequency-shift keying with modulation index h=1/M, where M is the size of the symbol alphabet. The results of codes searches show that there is very little loss in squared Euclidean distance when comparing differential codes with coherent codes.