Hauppauge Signal Monitor Download [UPDATED]

The signal quality monitor is part of the WinTV v10 application. To show the signal quality, tune to a TV channel and hit 'CTRL-D'. The key indicator is Quality which shows the relative quality of the TV channel which is being displayed in the WinTV window.

If you cannot get a good TV picture after positioning the antenna, the digital TV signal is too weak to receive a Digital TV program without distortion. In this case, you might want to consider an antenna "booster" or a better TV antenna. Here are some tips from our TV Troubleshooting guide to help optimize the TV reception for over-the-air signals.

Hauppauge Signal Monitor Download

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If you are watching clear QAM digital cable TV or DVB-C, you can use the WinTV's signal quality display to see the reception quality of that channel. As with over-the-air TV signal strength, if the signal Quality is low, you might want to check your cable system in your home and either remove splitters (each splitter you have on a home cable network lowers the signal strength) or use a "cable TV signal booster".

The Signal Monitor needs some modifications in order to run with the WinTV-quadHD. There is a small batch file which runs 4 occurances of the Signal Monitor so that you can see the signal strength on all four tuners.

The WinTV Signal Strength Monitor displays the relative strength of the digital signal on the channel you are tuned to. It will show the Signal to Noise ratio, the number of Received Errors and the number of Uncorrectable errors.

The Digital Signal Monitor will not work with analog TV channels. It will work with ATSC and QAM digital TV channels on most Hauppauge digital TV tuner boards.

The Signal Monitor is a handy tool which can be used to either adjust your digital TV antenna, or when used with digital cable TV, to know if your digital signal is too weak to receive a signal without distortion.

(Example based on Sky Digital) The two cables are each connected to a different output on the LNB (the part at the end of the dish) and it is not a one way signal - the box can only receive on either 11Ghz or 12Ghz then scan vertical or horizontal at once (so one of four).

In this context, hybrid WASNs [5] may play a significant role in the large-scale deployment of this kind of noise monitoring networks both in terms of cost and extent of coverage. These networks combine high-capacity (Hi-Cap) nodes with cheaper low-capacity (Lo-Cap) nodes, which operate as masters and slaves in the network, respectively (see Figure 1). This architecture allows sensing places where the power supply cannot easily be provided by means of solar panels or other alternative energy sources that supply the Lo-Cap sensors. Both nodes typically compute the A-weighted equivalent noise level (LAeq) of the monitored acoustic environment [17], being the Hi-Cap nodes also responsible for data communications and any other type of complex processes, e.g., acoustic signal processing, recordings, etc. In [11], the authors present the design of an acoustic sensor network based on this approach, with basic nodes using a low power Controller (C). RUMEUR network [18] is also an hybrid WASN, including both high-accuracy equipment for critical places, combined with less-precise measuring equipment in other locations whose purpose is only updating the noise map in terms of LAeq.

With the same focus, the DYNAMAP LIFE project is aimed at developing a dynamic road traffic noise mapping system to represent the acoustic impact of road infrastructures in real-time in two Italian pilot areas: a suburban environment in Rome and an urban environment in Milan [19]. To do so, the project envisions a hybrid low-cost WASN including both Hi-Cap and Lo-Cap salve sensors, which will be located in places with limited power supply. The DYNAMAP project takes into account a noise monitoring challenge not faced by the aforementioned projects as it is only focused on one specific noise source: road traffic noise. Hence, it considers the inclusion of an Anomalous Noise Event Detector (ANED) [15] to provide a reliable picture of the actual RTN by minimizing the influence of other anomalous noise events [20]. Up to now, the ANED algorithm has been designed as a two-class classifier (ANE vs. RTN) using Mel Frequency Cepstral Coefficients (MFCC) [21] as acoustic parametrization and supervised machine learning classification to run in real-time on the Hi-Cap sensors of the hybrid WASN, showing promising results on real-life data. However, the Hi-Cap ANED algorithm [15] cannot be implemented as originally designed in the slave Lo-Cap sensors due to the computational resources it demands. Nevertheless, it would be desirable to include an adapted version of the algorithm to run on the Lo-Cap sensors in order to provide an homogeneous picture of the RTN, thus, allowing the hybrid WASN to discard ANE from the LAeq computation also in those locations where the slave sensors will be placed.

The first category of WASN that can be found in the literature is built to achieve high accuracy and reliability, together with low noise floor. To that effect, most of their acoustic sensors are monitoring stations from Bruel and Kjaer [29] or Larson and Davis [30], which are equipped with IEC class 1 microphones. Those projects working with this kind of sensors are mainly focused on performing a detailed study of the acoustic environment of the city of interest. In [13,31], the FI-Sonic project based on the FIWARE platform is described; it consists of an acoustic sensor network based on ambisonics microphones, a multichannel acquisition card (from 2 to 128 GB), a network interface (with a Wi-Fi/3G modem) and a media server, its main processing unit, which runs the audio analyses. The collected information is used to create quasi-real-time dynamic noise and event maps, as well as to identify specific pre-trained sound sources for surveillance purposes. The FI-Sonic project is an example of the application of high accuracy WASNs to noise monitoring, but its pervasive deployment will require a very high investment. The problem associated with this first category of WASN is the price of the deployment of an entire network with several nodes, which may become prohibitive.

Block diagram of the acoustic signal processing within the Lo-Cap acoustic sensor. The upper part details the block diagram of the ANED Lo-Cap, with a binary label as an output, and the lower branch details the evaluation of the LAeq of the measured acoustic signal.

where PANE and PRTN are the a priori probabilities of class ANE and class RTN, respectively, and xmin and xmax are the minimum and maximum observed signal energy level at frequency subband i, respectively.

2D-PDF of the ANE (left) and the RTN (right) of the suburban dataset. The frequency subband index i corresponding to the MFS is labelled in the x-axis (the reader can find the corresponding frequency in Table 1), while the corresponding logarithmic signal level at each subband is depicted in the y-axis in dBs. The Figures colormap is blue for lower probabilities while tends to warm colors (with the maximum in red) for higher probabilities. The solid white line represents the optimum decision threshold for the one-band linear discriminant classifier at each frequency bin.

It is worth noting the high similarity of the 2D-PDFs of both ANE and RTN plots for the suburban acoustic environment, showing also an important overlap between them. However, the ANE class presents a slightly wider variance than the RTN class along the signal level axis for most of the MFS (i.e., an increase of about 10 % in the variance values for the ANE class is found with regard the RTN variance). However, a decrease of this signal level dispersion of both distributions can be observed along the frequency subband indexes from 23 to 27. Then, within those subbands the overlap of both signal level distributions (ANE and RTN) are lower, being the ANE class the one that attains higher signal levels (e.g., its 2D-PDF maximum probabilities, represented in yellow color, are placed closer to the optimum decision threshold). Another subband to analyze is number 2, which presents a lower value than subbands 1 and 3 in the analysis of both ANE and RTN. Subband 2 corresponds to a central frequency of 153 Hz, which is one of the most common frequency ranges of road traffic noise [42,43], and yet in this location the curve of the threshold adapts to the best possible discrimination between ANE and RTN following Equations (2) and (3).

2D-PDF of the ANE (left) and the RTN (right) of the urban dataset. The frequency subband index i corresponding to the MFS is labelled in the x-axis (the reader can find the corresponding frequency in Table 1), while the corresponding logarithmic signal level at each subband is depicted in the y-axis in dBs. The Figures colormap is blue for lower probabilities while tends to warm colors (with the maximum in red) for higher probabilities. The solid white line draws the optimum decision threshold for the one-band linear discriminant classifier at each frequency bin.

The results in the suburban scenario considering all the Mel-based subbands (M = 48) yield a Macro-averaged F1 measure [55] of 51.6% (with a F1 = 0.37). Although this accuracy is quite low, it is to note that it is indeed higher than the one obtained using a non-optimized threshold, i.e., a threshold based on an uniform random selection within the range of the measured signal energies. In this case, the ANED Lo-Cap accuracy decreases up to 50.4%. If we compare the F1 values obtained by the ANED Lo-Cap and the ANED Hi-Cap [15] in this scenario, we can observe that the subband optimized ANED Lo-Cap is around 9% less accurate on average, but allowing a computational load decrease in an order of 6 times (see Section 5). Finally, we want to note that some preliminary tests considering subband selection show promising results, with an averaged increase of around 3% in the F1 measure with respect to the full-band ANED Lo-Cap. 006ab0faaa