Re: Pinguin Audio Meter 4.5 Prol
Icaro Aveiga
Blue Cat's DP Meter Pro (DPMP) is a unique audio analysis tool that combines very flexible and customizable audio meters with advanced side chaining control capabilities thanks to its MIDI and automation outputs.
The metering capabilities of the DPMP plug-in let you control and monitor audio signals exactly the way you want: almost every aspect of the peak, RMS and crest factor meters can be customized. It proposes five different scales by default, including Bob Katz's popular K-System scales (K-12, K-14, K-20), and you can create your own.
Digital television in the USA and Canada[12]includes a mechanism known as metadata that can be used to correct loudness differences between programmes. Metadata is information that describes various characteristics of the audio signal, and travels along with the audio bitstream of a given programme. In the ATSC system, the metadata parameter that indicates loudness is called dialnorm. It informs devices of the loudness level of the incoming signal, thus allowing these devices (such as the home audio decoder) to make the necessary corrections to ensure that all programmes are played at the same preset loudness level.
The most commonly used instruments for monitoring and measuring time-varying audio signal levels are volume-unit (VU) and peak-programme (PPM) meters. The fast-reacting nature of the PPM meter makes it particularly useful to monitor peak signal levels and the usage of available headroom. For this reason, PPM measurement specifications have been an important component in broadcast guidelines. However, neither instrument indicates loudness. Rather, the operator must infer loudness from the continuously changing meter readings, a process requiring interpretation skills which may introduce errors and inconsistencies. A further shortcoming of the VU or PPM meter is that neither account for the frequency selectivity of the human ear.
To further validate the performance of the meter selected by the SRG, two additional rounds of subjective tests were conducted at the CRC with this meter only. The first of these two additional tests used 96 new monophonic audio sequences while the second used 144 mono, stereo and multichannel sequences [8]. The performance of the ITU-R loudness meter is shown in Figure 1 where objective versus subjective loudness is plotted for all three subjective tests, totaling 336 audio sequences (mono, stereo and multichannel). A correlation of 0.977 indicates a very good agreement between objective and subjective loudness. Subsequent subjective testing by other researchers confirmed the performance of the ITU-R loudness algorithm relative to more complex psychoacoustic models[9].
In parallel with the efforts of the ATSC, members of the European Broadcast Union (EBU) established a study group (P/LOUD) to investigate new practices with the goal of providing more consistent loudness levels to the listener. The scope of their work was to include radio as well as television systems that did not include audio metadata. Their work also included investigating new forms of loudness and peak signal metering to complement or to replace existing VU and PPM meters.
On-animal sensors are the most common remote sensing devices deployed in animal ecology10. They are primarily used to acquire movement trajectories (i.e., GPS data) of animals, which can then be classified into activity types that relate to the behavior of individuals or social groups10,64. Secondary sensors, such as microphones, video cameras, heart rate monitors, and accelerometers, allow researchers to capture environmental, physiological, and behavioral data concurrently with movement data65. However, power supply and data storage and transmission limitations of bio-logging devices are driving efforts to optimize sampling protocols or pre-process data in order to conserve these resources and prolong the life of the devices. For example, on-board processing solutions can use data from low-cost sensors to identify behaviors of interest and engage resource-intensive sensors only when these behaviors are being performed66. Other on-board algorithms classify raw data into behavioral states to reduce the volume of data to be transmitted67. Various supervised ML methods have shown their potential in automating behavior analysis from accelerometer data68,69, identifying behavioral state from trajectories70, and predicting animal movement71.
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