Search Results (5)

Automatic classification of dog vocalizations can support bioacoustic monitoring and animal welfare, but many systems require spectral or cepstral preprocessing. This study evaluates a lightweight one-dimensional convolutional neural network (1D-CNN) for Bark and Howl classification directly from raw waveforms under controlled additive noise. The dataset comprised 46 Bark and 57 Howl recordings. Audio was converted to mono, resampled to 16 kHz, and standardized to 2.0 s. The network contains 7130 trainable parameters, occupies 27.85 KB with 32-bit weights, and requires 18.35 MFLOPs. The complete five-fold cross-validation procedure was repeated ten times with independently generated run-specific seeds and newly shuffled partitions. Under the no-added-noise condition, mean accuracy was 93.40 ± 2.62%, and macro F1-score was 93.20 ± 2.75%. Performance remained within run-to-run variability between 30 and 5 dB SNR for Gaussian and uniform additive noise, whereas mean accuracy decreased to 79.42% at 0 dB. In the seed-42 reference ablation, removing noise augmentation preserved no-added-noise accuracy but reduced 5 dB accuracy by approximately 20 percentage points. The findings provide preliminary recording-level evidence for efficient Bark and Howl classification under controlled conditions. Generalization to unseen dogs and field recordings remains unverified. Full article

This study investigated context-associated variation in vocalizations in Malinois dogs through acoustic parameter analysis. Vocalizations from thirty adult Malinois dogs (15 males, 15 females) aged 2 to 3 years were recorded across 11 behaviourally defined contexts. Using Praat software, key acoustic parameters—fundamental frequency (F0), harmonic-to-noise ratio (HNR), and formant frequencies—were extracted and analyzed. Results indicated that different vocalization types (barking, whimpering, growling, snarling, howling) exhibited distinct acoustic profiles. Whimpering and howling showed significantly higher F0 values than barking (p < 0.05), with whimpering uniquely displaying both low and high F0 components. Dogs in contexts expected to be positively valenced (e.g., food anticipation) showed lower HNR than those in contexts expected to be negatively valenced (e.g., separation) (p < 0.05). However, the actual internal states were not independently verified. Formant analysis revealed that snarling and howling had lower Formant 1 (F1) values (p < 0.05), while formant dispersion varied with emotional state. These findings suggest that acoustic analysis of dog vocalizations can provide objective insights into dogs’ motivational and arousal changes, thereby improving our understanding of canine vocal communication, social behavior, and the human–dog bond. This approach has potential applications for working-line Malinois breeding programs and for enhancing human–working dog interactions. Full article

Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis. The system uses a preprocessing step to segment relevant sound regions from audio recordings, followed by feature extraction using Short-Time Fourier Transform (STFT), Mel-frequency cepstral coefficients (MFCCs), and linear-frequency cepstral coefficients (LFCCs). These features are input into convolutional neural network (CNN) classifiers to evaluate performance. Experimental results demonstrate the effectiveness of different CNN models and feature extraction methods, with AlexNet, DenseNet, EfficientNet, ResNet50, and ResNet152 being evaluated. The system achieves high accuracy in classifying vocal behaviors, such as barking and howling in dogs, providing a robust tool for behavioral analysis. The study highlights the importance of automated systems in bioacoustics research and suggests future improvements using deep learning-based methods for enhanced classification performance. Full article

This study examined how separation behavior differs between dogs with and without separation-related problem behavior (SRB) and the possible risk factors. The study consisted of an online survey with 940 dog holders, which, in addition to demographic facts, also includes personality, emotional disposition and the attachment by the holder. Furthermore, a separation test was carried out with six non-SRB dogs over a maximum of 6 h, in which behavior and cortisol were determined. The questionnaire revealed that SRB dogs differed significantly from non-SRB dogs regarding the following factors: symptoms with at least a medium effect size such as restlessness, excitement, whining, howling, lip licking, barking and salivation, time to relax after separation, pessimism, persistence, excitability, calmness, separation frequency, greeting of holder and type of greeting. There were several other differences, but with weak effect sizes. The test showed that non-SRB dogs were mostly inactive during separation (lying resting and lying alert). Vocalization was almost non-existent. Behavior and cortisol did not change significantly over the different time periods. The data demonstrated typical symptoms and possible risk factors, some of which may be avoided or changed to improve animal welfare. Full article

The use of IoT (Internet of Things) technology for the management of pet dogs left alone at home is increasing. This includes tasks such as automatic feeding, operation of play equipment, and location detection. Classification of the vocalizations of pet dogs using information from a sound sensor is an important method to analyze the behavior or emotions of dogs that are left alone. These sounds should be acquired by attaching the IoT sound sensor to the dog, and then classifying the sound events (e.g., barking, growling, howling, and whining). However, sound sensors tend to transmit large amounts of data and consume considerable amounts of power, which presents issues in the case of resource-constrained IoT sensor devices. In this paper, we propose a way to classify pet dog sound events and improve resource efficiency without significant degradation of accuracy. To achieve this, we only acquire the intensity data of sounds by using a relatively resource-efficient noise sensor. This presents issues as well, since it is difficult to achieve sufficient classification accuracy using only intensity data due to the loss of information from the sound events. To address this problem and avoid significant degradation of classification accuracy, we apply long short-term memory-fully convolutional network (LSTM-FCN), which is a deep learning method, to analyze time-series data, and exploit bicubic interpolation. Based on experimental results, the proposed method based on noise sensors (i.e., Shapelet and LSTM-FCN for time-series) was found to improve energy efficiency by 10 times without significant degradation of accuracy compared to typical methods based on sound sensors (i.e., mel-frequency cepstrum coefficient (MFCC), spectrogram, and mel-spectrum for feature extraction, and support vector machine (SVM) and k-nearest neighbor (K-NN) for classification). Full article