The axolotl (Ambystoma mexicanum) has been used as a model organism for studying development, genetics, and regeneration. Although the sensory hair cells of the lateral line of this species have been shown to be able to regenerate, it is not known whether this also occurs in the inner ear. In fact, little is known about the hearing capabilities of the axolotl or other salamander species. I recorded auditory evoked potentials (AEPs) of six axolotls at eleven frequencies (0.1, 0.25, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 4, and 6 kHz) in order to produce baseline audiograms of underwater pressure sensitivity. Individuals were then subjected to a 48-hour, 150 Hz sound exposure at approximately 170 dB (re 1 μPa). AEPs were then performed to measure hearing thresholds immediately after sound exposure and at 2, 4, and 8 days post-sound exposure (DPSE). In the baseline audiogram, axolotls were most sensitive at 600 Hz, with an additional peak of sensitivity at 3 kHz. Following sound exposure, axolotls experienced a 6 to 12 dB temporary threshold shift (TTS) after sound exposure, with TTS being greatest at low frequencies near the 150 Hz stimulus frequency (i.e., 100 and 250 Hz). Hearing sensitivity returned to control levels within 8 DPSE. This indicates that axolotls do possess the ability to recover hearing sensitivity after damage following acoustical trauma. This study is the first to document hearing loss in the axolotl. Future studies are needed to correlate this hearing loss and recovery to sensory hair cell loss and regeneration in the axolotl inner ear.

In recent times robots are becoming more and more common. They are everywhere. Walking, running, swimming, flying and many of them have much in common with the creatures inhabiting this planet. A lot of it in order to make them appeal more to us, instead of simply being portrayed as stone cold machines. Continuing on the path evolution has laid out before us seems to be a wise decision to make, aspiring to efficiently utilize our knowledge about science and engineering with the vision of improving our future. With the intention to simulate a four legged animal and evaluate the means of interacting with one´s surrounding, a quadruped locomotion system together with two types of sound and voice interacting systems have been assessed. A demonstrator was built to test the real world problems and decide what kind of interacting that is most beneficial. The results indicate that voice commands and speech recognition, rather than sounds from the environment are more practical and robust as a way of interacting with one´s surroundings. / På senare tider har robotar blivit mer och mer vanliga. De är överallt. Gående, springande, simmande, flygande och många av dem har mycket gemensamt med de varelser som lever på denna jord. Mycket av detta för att tilltala oss mer, istället för att framstå som enbart iskalla maskiner. Att fortsätta på den väg som evolutionen har lagt framför oss verkar vara ett vist beslut att ta, i strävan efter att effektivt utnyttja våra kunskaper i vetenskap och ingenjörskonst med visionen om att förbättra vår framtid. Med målet att simulera ett fyrbent djur och utvärdera möjligheterna till att interagera med ens omgivning, har ett fyrbent förflyttningssystem tillsammans med två typer av ljud och röstsystem tagits fram. En prototyp kontruerades för att testa de problem som uppstår i den verkliga värden och för att kunna bedöma vilket sätt att interagera som visar vara sig mest fördelaktigt. Resultaten indikerar att röstkommandon och röstigenkänning, snarare än ljuddetektion från omgivningen är mer praktiska och robusta som ett sätt att interagera med sin närmiljö.

Submitted by Fernando Souza (fernandoafsou@gmail.com) on 2017-08-11T14:47:59Z No. of bitstreams: 1 arquivototal.pdf: 3075980 bytes, checksum: 34017b499d4b0a096285315cb614b985 (MD5) / Made available in DSpace on 2017-08-11T14:47:59Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 3075980 bytes, checksum: 34017b499d4b0a096285315cb614b985 (MD5) Previous issue date: 2015-12-04 / The urban violence has been increasing in almost Brazilian state and in order to face this threat, new technological tools are required by the police authorities in order to support their decisions on how and when the few available resources should be employed to combat criminality. In this context, this work presents an embedded computational tool that is suitable for detecting gun-shots automatically. To provide the necessary knowledge to understand the work, a brief description about impulsive sounds, re guns and the gun-shot characteristics are initially presented. Latter, a system based on modules is proposed to detect and recognize impulsive sound, which are characteristics of gun-shots. However, since the system contain several modules in this work we have focus only on two of them: the module for detecting impulsive sounds and the module for distinguish a gun-shot from any other impulsive sound. For the impulsive detection module, three well-known algorithms were analyzed on the same condition: the fourth derivative of the Root Median Square (RMS), the Conditional Median Filter (CMF) and the Variance Method (VM). The algorithms were tested based on four measured performance parameters: accuracy, precision, sensibility and speci city. And in order to determine the most e cient algorithm for detecting impulsive sounds, a cadence test with impulsive sounds, without or with additional noise (constant or increasing) was performed. After this analysis, the parameters employed on the CMF and VM method were tested in a wide range of con gurations to verify any possibility of optimization. Once this optimal method was determined, the classi cation module to recognize gun-shots started to be implemented. For this, two distinguish methods were compared, one based on the signal wrapped over the time and the other based on most relevant frequencies obtained from the Fourier transform. From the comparison between the two methods it was observed that the wrapped method provided 54% of accuracy in the classi cation of impulsive sounds, while with the frequency analysis this value was 72%. / A violência urbana vem crescendo anualmente em praticamente todos os estados brasileiros e para fazer face a essa amea ca, as autoridades policiais necessitam cada vez mais de ferramentas tecnológicas que os auxiliem na tomada de decisões sobre quando e como empregar os parcos recursos disponíveis a repressão do crime. Neste contexto, e apresentado nesse trabalho uma ferramenta computacional, passível de ser embarcada em dispositivos m oveis, que possibilita realizar a detecção e reconhecimento automático de disparos de armas de fogo. Para tanto, são descritos inicialmente os fundamentos básicos sobre sons impulsivos, armas de fogo e caracter sticas de disparos. Posteriormente, descreve-se uma proposta de um sistema modular de detecção e reconhecimento de disparos. No entanto, devido ao sistema conter diversos m odulos complexos, este trabalho teve foco em dois deles: o modulo de detecção de sons impulsivos e o modulo de classificação, que permite distinguir disparos de armas de fogo de outros sons impulsivos. Para o módulo de detecção de sons impulsivos foram analisados três algoritmos amplamente descritos na literatura: o algoritmo da quarta derivada da RMS, o da Conditional Median Filter (CMF) e o Método da Variância (VM). Os algoritmos foram testados com base nas medidas de desempenho da acurácia, precisão, sensibilidade e especificidade. E a para determinar o método mais e ciente, foram realizados testes de cadências, com sons impulsivos sem adição de ru do sonoro, com adição de ruído constante e com ruído variável. Ao final dessa anáise, os par^ametros preconizados na literatura para os m etodos CMF e VM foram alterados para uma verificação de possibilidade de otimização. De nido o algoritmo de detecção de impulso mais e ciente, iniciou-se o desenvolvimento do módulo de classificação. Para isso, foram propostas duas t ecnicas para o reconhecimento de disparos de armas de fogo, uma utilizando uma compara c~ao da envolt oria do som no dom nio do tempo e outra baseada na comparação de frequências dominantes obtidas por meio da transformada de Fourier. Numa comparação entre as duas técnicas observou-se que com a técnica da envoltória e poss vel identi car 54% dos sons impulsivos, enquanto que com a t ecnica baseada no dom nio da frequ^encia, este percentual foi de 72%.

This master's thesis deals with the design and development of mobile aplication for Android platform. The aim of the work is to implement a simple and user-friendly user interface that would support and assist the user in trainning and sport exercises. The thesis also include implementation of sound detection to support during exercises and voice instruction by application. In practice the application should help in making training exercises more comfortable without the user being forced to keep mobile device in hand.

No description available.

Recently, Deep Learning has revolutionized many fields, where one such area is Voice Activity Detection (VAD). This is of great interest to sectors of society concerned with detecting speech in sound signals. One such sector is the police, where criminal investigations regularly involve analysis of audio material. Convolutional Neural Networks (CNN) have recently become the state-of-the-art method of detecting speech in audio. But so far, understanding the impact of noise and sampling rates on such methods remains incomplete. Additionally, there are evaluation metrics from neighboring fields that remain unintegrated into VAD. We trained on four different sampling rates and found that changing the sampling rate could have dramatic effects on the results. As such, we recommend explicitly evaluating CNN-based VAD systems on pertinent sampling rates. Further, with increasing amounts of white Gaussian noise, we observed better performance by increasing the capacity of our Gated Recurrent Unit (GRU). Finally, we discuss how careful consideration is necessary when choosing a main evaluation metric, leading us to recommend Polyphonic Sound Detection Score (PSDS).