Tomasz Słowik

Spectral Features for Audio Based Vehicle Identification

In this paper we address automatic vehicle identification based on audio information. Such data are complicated, as they depend on vehicle type, tires, speed and its change. In our previous research we designed a feature set for selected vehicle classes, discriminating pairs of classes. Now, we decided to expand the feature vector and find the best feature set (mainly based on spectral descriptors), possibly representative for each investigated vehicle category, which can be applied to a bigger data set, with more classes. The paper also shows problems related to vehicles classification, which is detailed in official documents by national authority for issues related to the national road system, but simplified for automatic identification purposes. Experiments on audio-based vehicle type identification are presented and conclusions are shown.

Spectral features for audio based vehicle and engine classification

In this paper we address automatic vehicle and engine identification based on audio information. Such data depend on many factors, including vehicle type, tires, speed and its change, as well as road type. In our previous research we designed a feature set for selected vehicle classes, discriminating pairs of classes. Later, we decided to expand the feature vector and find the best feature set (mainly based on spectral descriptors), possibly representative for each investigated vehicle category, which can be applied to a bigger data set, with more classes. The experiments were performed first on on-road recordings, and then continued with test bench (dyno) recordings. The paper also shows problems related to vehicles classification, which is detailed in official documents by national authority for issues related to the national road system, but simplified for automatic identification purposes. Experiments on audio-based vehicle type and engine type identification are presented and conclusions are shown.

Audio-Based Speed Change Classification for Vehicles

Vehicle speed is an important factor influencing highway traffic safety. Radars are applied to control the speed of vehicles, but the drivers often decelerate when approaching radar, and then accelerate after passing by. We address automatic recognition of speed change from audio data, based on recordings taken in controlled conditions. Data description and classification experiments illustrate both changing speed and maintaining constant speed. This is a starting point to investigate what percentage of drivers actually maintain constant speed, or slow down only to speed up immediately afterwards. Automatic classification and building an appropriate database can help improving traffic safety.

Time-Frequency Representations for Speed Change Classification: A Pilot Study

Speeding is an important factor influencing road traffic safety. Even though speed is monitored using radars, the drivers may increase speed after passing the radar. In this paper, we address automatic classification of speed changes (or maintaining constant speed) from audio data, as a microphone added to the radar can register the drivers’ behavior both in front of and behind the radar. We propose two time-frequency based approaches to represent the audio data for speed classification purposes. These approaches have been tested in a pilot study using on-road data, and the results are presented in this paper.

Emission of Fumes of Ursus MF235 under Conditions of Load with The Use of a Movable Dynamometric Stand

Abstract The article presents calculations of unit fumes emission made based on the measurements of the fumes composition during a load test. The test was carried out with the use of a movable dynamometric stand adjusted for testing agricultural tractors by power take-off shaft. Calculations of unit emission were made based on the assumed flow of air sucked by an engine and the measured composition of fumes emitted during the load test. Results of tests and analyses supply information on the mass of particle matter and the selected gas elements emitted by an engine working under the load which was related to simultaneous determination of the exploitation characteristic of an engine. The presented methodology may be helpful at evaluation of impact of farm tractors on the environment within the hazardous fume components emitted by them.