Using the beat histogram for speech rhythm description and language identification

In this paper we present a novel approach for the description of speech rhythm and the extraction of rhythm-related features for automatic language identification (LID). Previous methods have extracted speech rhythm through the calculation of features based on salient elements of speech such as consonants, vowels and syllables. We present how an automatic rhythm extraction method borrowed from music information retrieval, the beat histogram, can be adapted for the analysis of speech rhythm by defining the most relevant novelty functions in the speech signal and extracting features describing their periodicities. We have evaluated those features in a rhythm-based LID task for two multilingual speech corpora using support vector machines, including feature selection methods to identify the most informative descriptors. Results suggest that the method is successful in describing speech rhythm and provides LID classification accuracy comparable to or better than that of other approaches, without the need for a preceding segmentation or annotation of the speech signal. Concerning rhythm typology, the rhythm class hypothesis in its original form seems to be only partly confirmed by our results

Beat histogram features for rhythm-based musical genre classification using multiple novelty functions

In this paper we present beat histogram features for multiple level rhythm description and evaluate them in a musical genre classification task. Audio features pertaining to various musical content categories and their related novelty functions are extracted as a basis for the creation of beat histograms. The proposed features capture not only amplitude, but also tonal and general spectral changes in the signal, aiming to represent as much rhythmic information as possible. The most and least informative features are identified through feature selection methods and are then tested using Support Vector Machines on five genre datasets concerning classification accuracy against a baseline feature set. Results show that the presented features provide comparable classification accuracy with respect to other genre classification approaches using periodicity histograms and display a performance close to that of much more elaborate up-to-date approaches for rhythm description. The use of bar boundary annotations for the texture frames has provided an improvement for the dance-oriented Ballroom dataset. The comparably small number of descriptors and the possibility of evaluating the influence of specific signal components to the general rhythmic content encourage the further use of the method in rhythm description tasks

Speaker Identification for Swiss German with Spectral and Rhythm Features

We present results of speech rhythm analysis for automatic speaker identification. We expand previous experiments using similar methods for language identification. Features describing the rhythmic properties of salient changes in signal components are extracted and used in an speaker identification task to determine to which extent they are descriptive of speaker variability. We also test the performance of state-of-the-art but simple-to-extract frame-based features. The paper focus is the evaluation on one corpus (swiss german, TEVOID) using support vector machines. Results suggest that the general spectral features can provide very good performance on this dataset, whereas the rhythm features are not as successful in the task, indicating either the lack of suitability for this task or the dataset specificity

Acoustic Identification of Flat Spots On Wheels Using Different Machine Learning Techniques

BMBF, 01IS18049B, ALICE III - Autonomes Lernen in komplexen Umgebungen 3 (Autonomous Learning in Complex Environments 3

Speech and music discrimination: Human detection of differences between music and speech based on rhythm

Rhythm in speech and singing forms one of its basic acoustic components. Therefore, it is interesting to investigate the capability of subjects to distinguish between speech and singing when only the rhythm remains as an acoustic cue. For this study we developed a method to eliminate all linguistic components but rhythm from the speech and singing signals. The study was conducted online and participants could listen to the stimuli via loudspeakers or headphones. The analysis of the survey shows that people are able to significantly discriminate between speech and singing after they have been altered. Furthermore, our results reveal specific features, which supported participants in their decision, such as differences in regularity and tempo between singing and speech samples. The hypothesis that music trained people perform more successfully on the task was not proved. The results of the study are important for the understanding of the structure of and differences between speech and singing, for the use in further studies and for future application in the field of speech recognition

Resilience of buildings to extreme weather events

Our climate is changing and the results of that are already visible. The two-month period of December 2013 and January 2014 was for England and Wales one of, if not the most, exceptional periods of winter rainfall in at least 248 years. In addition to that, on July 1st, 2015, at Heathrow, Greater London the highest July temperature on record for the UK was recorded. Our buildings are already performing poorly under the current weather conditions. Even the buildings that are performing well now may become intolerable for the occupants by 2080. It is therefore important to find ways to increase the resilience of the current building stock and to identify master planning principles for the new buildings. The scope of this research was to study the resilience of three different types of buildings (high- , medium- and low risk) under extreme weather conditions. The extreme events that were investigated are extreme hot and heavy rain. EDSL TAS, XP SWMM and MicroDrainage simulation software packages were used in order to estimate the thermal and energy performance of the buildings and investigate the effects of heavy rainfall. There is clear evidence to show that climate change is happening. According to the UK Climate Projections (UKCP09), we can expect warmer and wetter winters, hotter and drier summers, rising sea levels, and more extreme weather events. These extreme weather events in the UK are likely to increase with rising temperatures, causing among others more substantial rainfall events with an increased risk of flooding. Flooding is currently identified as one of the greatest threats to the UK posed by climate change. In addition to that, the UKCP09 show that means daily temperatures will increase everywhere in the United Kingdom. This will significantly affect the thermal and energy performance of the current building stock. This study presents four case studies where the resilience of the examined buildings is investigated under extreme hot weather events. It looks into the risk of overheating of a school building in 14 locations in the United Kingdom using the overheating criteria defined in Building Bulletin 101 (BB101). It examines three different ventilation modes and quantifies the required amount of cooling loads to achieve thermal comfort conditions. Furthermore, it considers the effect of relative humidity for an office building in London and for the same building, it examines the effect of the window-to-wall ratio on thermal comfort and energy consumption. This study also evaluates the effect of extreme rainfall events on the resilience of buildings and presents two case studies on this. The first one examines the effect of building development on the risk of flooding under extreme rainfall for an area that has a very low chance of flooding by modelling two different scenarios of building development. The second case study investigates the effects of sustainable drainage systems on residential developments under extreme rainfall events. The outcomes of this research present practical approaches of mitigating the effects of extreme hot weather and extreme rainfall events. The research has demonstrated that lower window to wall ratios result in more comfortable conditions and has also shown that a relative humidity control will result in improved thermal comfort conditions for most of the occupied hours during the summer months. This study has also examined a school building and quantified the amount of cooling loads required to comply with the BB101 criteria and presented a comparison between the current and future weather conditions. Additionally, the research results demonstrated that automated control of the opening of the windows results in reduced operative temperatures and improved thermal comfort conditions. This study has also investigated the effects of sustainable drainage systems (SuDS) during extreme rainfall and has quantified the effect of three different types of SuDS (permeable water, rainwater harvesting, and attenuation basins) for a new build residential development. It has also shown that building development will increase the risk of flooding from surface water, even for areas with a low chance of flooding

Eine qualitative Untersuchung der Generalisierungsverhaltens von CNNs zur Instrumentenerkennung

Künstliche neuronale Netze (ANNs) haben sich im Bereich des maschinellen Lernens für Audiodaten als erfolgreichstes Werkzeug mit hoher Klassifikationsrate etabliert [1]. Ein bedeutender Nachteil besteht aus wissenschaftlicher Sicht jedoch in der schweren Interpretierbarkeit des von ANNs tatsächlich gelernten Inhalts [2, 3]. Um dieses Problem anzugehen untersuchen wir in dieser Arbeit den Lern- und Generalisierungsprozess eines Convolutional Neural Networks (CNNs) für Multi-Label Instrumentenerkennung in den Hidden Layers des Netzwerks. Wir betrachten die unterschiedlichen Aktivierungen aller Layers durch unterschiedliche Instrumentenklassen um nachzuvollziehen, ab welcher Tiefe das Netzwerk in der Lage ist, zwei von der gleichen Klasse stammenden Stimuli als ähnlich zu erkennen. Wir wiederholen das Experiment mit den gleichen Stimuli für ein auf die Erkennung von vier Emotionen trainiertes CNNs. Dabei bestätigen sich einerseits viele unserer Betrachtungen zum Generalisierungsprozess, gleichzeitig lassen die Ergebnisse darauf schließen, dass das auf Emotionserkennung trainierte Netzwerk in der Lage ist, instrumententypische Patterns zu lernen

Automated natural ventilation and lighting strategy for a residential building under extreme hot weather

Automated control systems, intelligent HVAC and smart lighting can help reduce the energy consumption of the buildings and improve the thermal comfort conditions of the occupants. This study considers a typical detached residential building in the United Kingdom and examines the effect of an automated natural ventilation and lighting strategy on the energy consumption of the building and the thermal comfort of the occupants. For the purpose of this study the windows of two bedrooms of the examined building are modelled with a temperature-based control function and appropriate target illuminance levels have been set to control the lighting. This paper examines the week with the higher external temperature and uses dynamic thermal simulations in order to assess the performance of the building. Simulations are performed with EDSL TAS software using the latest Design Summer Year (DSY) weather files from CIBSE. Results of the simulations show that an automated window opening system can reduce the operative temperatures up to 4°C, improve thermal comfort conditions and reduce lighting gains by 49%