Loudness Management for Home Television Viewing Amal Punchihewa, Sari Kilani
Emirul Haizart
School of Engineering & Advanced Technology Massey University Palmerston North, New Zealand
[email protected]
Kuala Lumpur, Malaysia
Abstract— This paper presents dual sensor based loudness management system for television viewing at home environment in compliance with BS1770 loudness measurement standard. The aim of the research was mainly to address the issue of sudden increase in loudness during commercials in between TV programmes. Same approach works for any TV programme as well. The scope of this research reported here was limited to investigate, develop an adaptive loudness control system to create a pleasurable TV viewing environment and to prove efficacy of algorithm functionality by simulations. The novel contributions made in paper are the design and the proof of functional efficacy of the loudness management algorithms using the loudness measurements made through the simple instrumentation set-up in compliance with BS1770 loudness measurement standard. Keywords-loudness; televison sound; BS1770 standard; FPGA;
I.
INTRODUCTION
The complains from TV viewers regarding the varying level of loudness are not something unheard of in the TV broadcasting industry [1], [2]. The jumping of loudness level in between TV programmes and commercials is annoying for the viewers and they often have to resort to regularly reducing the volume level using the remote controller. The problem is that there were no measurement algorithms at user end that can perform real-time instrumentation. The research investigated and developed an adaptive loudness control system to be implemented at the user end to create a pleasurable television viewing environment. Fig. 1 shows a block diagram of the conceptual idea.
has been done in relation to the loudness problem. The section III builds the methodology with reasons behind the loudness problem and a detailed description of the modules that are involved in this research project. Section four presents the results and findings highlighting the contributions made to loudness instrumentation. Matlab simulation
Hardware Simulation
Figure 2. Block diagram of the approach to devleop a hardware implementation
Fig. 2 shows the three phases of the larger research project. In the first phase of research that is reported here, the proposed and developed algorithms will be simulated within the MatlabTM simulation environment. Then in the second phase of the research, measurement and control algorithms will be coded using high-level hardware description language HandleC using DK design suite like an environment that supports a programmable logic device. FPGA will selected and C-like Handle-C coding environment will be used to code the algorithm for FPGA. to enable real-time processing. The scope of this paper was restricted to design of measurement and an adaptive control system at the user end that is simulated using MatlabTM and sound captured using a human head mimicking model. Hence the two main objectives of research reported in this paper includes the implementation of the loudness measurement algorithm based on International Telecommunication Union Radiocommunication division (ITU-R) called BS.1770-1 and to determine the best timewindow size to be used for the measurement algorithm.
II.
Figure 1. Block diagram of the system
This paper has been organized as follows. Firstly, scope of the paper has been established in introduction followed by section II with background that provides some history of the loudness problem, current situation and the previous work that
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Implementation Testing
BACKGROUND
A. History of Loudness Problem The issue of dramatic changes in perceived loudness between TV programmes and commercials dates back to at least 40 years ago in the UK and the United States while the attempt in solving this problem goes back to as early as the beginning of 1970s at Columbia Broadcasting System (CBS) [2]. In 1966 Bauer and Torick realizes the need for a loudness measurement tool. They also stated how the traditional tools that have been used to measure loudness; VU meter and sound level meter are not the most efficient tools for loudness measurement [3]. During the late 1980s an attempt to develop a loudness control system operating at the ingest stage was made by Thames Television together with ITC. However, this
effort that was made during the same time UK adopted the NICAM sound and digital distribution, was interrupted in 1992 when Thames Television loss their franchise [3]. In 2007, the ITU-R took the initiative to come up with an international loudness measurement standard called the BS.1770-1 [4]. This standard for loudness measurement that is long overdue will hopefully bring us a step closer to solving the issue. The issues discussed above shows that the problem of loudness variation is something that is still need serious attention.
B. Defition of Loudness In order to solve the problem concerning loudness there is a need to first understand the concept of loudness. Loudness is a subjective concept that depends on various parameters [5], [6]. In general loudness is the sound level that people hear or perceived [7]. In a more technical term loudness can be defined as follow:
preserve most of the sound effects while mitigating rapid loudness changes. Value of 2.5seconds was used here after for integration period denoted by T.
A. Loundness BS.1770-1 [4] standard defines the loudness as given in equation (1). T is the integration time and x is the audio signal. Reference value was selected as unity and capture system was calibrated using 1 kHz tone.
10 log
(1)
Previous research reports loudness dependence on frequency and the source. These are shown in Fig.4 and Fig. 5. Fig. 4 reports the equal loudness measured using phons depicting the equal loudness contours [11].
Loudness refers to the perceived strength of a piece of audio (music, speech, sound effects etc) that depends on various parameters such as frequency contents, duration and level amongst other things [5], [7]. Lund (2006) stated that loudness is subjected to Between Listener Variability (BLV) where differences in age, sex, culture etc contributes to the variation. He also stated that even individual assessments on loudness done by the same person can be inconsistent because of variation in the mood, time of the day, attention etc. This type of variation, Lund defines it as Within Listener Variation (WLV). The subjectivity of loudness makes it such a complicated entity to measure [7] and the inability to measure loudness correctly is one of the main reasons the problem of loudness variation could not be solved.
III.
METHODOLOGY
In the first phase, algorithms are developed and simulated in a software simulation environment using Matlab to check the functionality. Algorithms include the estimation of loudness levels for the audio captured using two microphones mimicking a television viewer as shown in Fig. 3 and an adaptive loudness control. MIC 1
Figure 4. Equal loudnes curves [11]
MIC 2
Buffer Figure 3. Sound capture like human using two microphones.
This two channel audio captured will be pre-processed to take into account the human auditory system. Then combined sound energy is used in software simulation of measurement and control algorithms within MatlabTM that prove the concept and functionality of the loudness measurement, management algorithms. According to the loudness measurement standard, sound energy needs to be integrated over a time to ascertain the level of loudness. Extensive informal test carried out using 20 video clips showed that integration period of 2 to 3 seconds can
Figure 5. Four different weighted curves for loudness curves for different sound sources [4]
Fig. 5 shows weighted curves for different loudness types such as aircraft sound etc. The sound that is captured needs to be pre-processed for human auditory system [4] and equalisation using a weighting curve. Then it is known as Leq.
L R
Sound Capture
Human head compensation
Loudness Weighting
channels. Leq(RLB) measure after applying a pre-filtering to account for acoustic effects that the head has on incoming signals where the head is represented as a rigid sphere. The pre-filter characteristics are shown in Fig. 8. These characteristics can be modelled by a digital high pass filter. The infinite impulse response recursive filter implementation coefficients are given in table I. TABLE I.
FILTER COEFFICIENTS FOR THE PRE-FILTER TO MODEL A SPHERICAL HEAD [1]
Figure 6. Signal processing chain of the captured audio.
The Leq algorithm operates on two monophonic signals L and R. The main advantage of this algorithm lies in its simplicity and scalability. It is simple since it is entirely made up of basic signal processing blocks that can be implemented in the time domain using inexpensive hardware. It scalable since the same processing is applied at each channel therefore it is easy to implement a meter to accommodate any number of channels from 1 to N. The Fig. 7 shows the configuration of the dual microphone based loudness metering to mimic the audio that a human in front of a television will hear. The audio captured using a model-human head fixed with two microphones were processed using a pre-filter and a RLB filter to account for human auditory system that is modeled at sampling frequency of 48KHz. Sampling frequency of 48 kHz was chosen as it the common high fidelity audio sampling frequency used in many digital media such as digital video broadcasting and DVD.
Figure 7. Loudness compuation based on ITU standard using two microphones.
Figure 9. RLB weighting curve based on weighted curves for loudness [1]
The value of the coefficients shown in the table I are the coefficients when the sampling rate is 48 kHz. If the sampling rate is different to 48 kHz, different coefficient values should be chosen to provide the same frequency response as the specified filter provides at 48 kHz. From the tests done it is shown that the performance of the algorithm is not affected by small variations in these coefficients. The second stage of the algorithm applies the RLB weighting curve, which consists of a simple high-pass filter like the pre-filter. B weighting curve shown in Fig. 9. The coefficient values for the high-pass infinite impulse response filter coefficients are shown in table II [1].
Figure 8. Response of the pre-filter used to account for the acoustic effects of the head [1].
The loudness Leq is measured by measuring each individual audio channels using the monophonic L and R audio
TABLE II.
FILTER COEFFICIENTS FOR THE RLB WEIGHTING CURVE [1]
Both the pre-filter and RLB weighting digital IIR filter can be drawn as shown in Fig. 10. These filter coefficients are for sampling rate of 48 kHz.
Figure 10. Schematic diagram for the RLB and pre-filter as a second order IIR filter
After the pre-filtering and the RLB filtering, the meansquare energy during the measurement interval or integration period T is obtained using the formula: y
(2) V.
Where yi = the squared value of the input signal filtered by both pre-filter and the RLB weighting curve. (i = Left and Right, N=2 where N is the number of audio channels). The value of zi is computed and multiplied with the gain factor for each channel before summing up for all N channels as to obtain Loudness: !"#$$ −0.691 + 10*!+ ∑/ - . 01
(3)
Gain factors for both left and right channels are unity. The frequency weighting used in this measure is K-weighting. The unit of measured loudness is LKFS which stands for Loudness K weighted relative to nominal full scale. The LKFS unit is similar to decibel where by an increase in the level of a signal by 1 dB results in the loudness reading to increase by 1 LKFS IV.
Figure 11. Loudness measurement of a televison commercial (top) origianl signal above -23dBLKFS, (bottom) Blue signal kept below -23dB LKFS
RESULTS AND DISCUSSION
As shown in Fig. 3 and Fig. 6, two channel audio were captured as a television viewer would hear. Then the digitised signals are acquired into the simulation environment and saved as a uncompressed file. Two audio files are then off-line processed as per equations (3). Using the measurement and control algorithms the loudness was kept within within the limits of standards ( -23dB LKFS) or any value pre-set by the viewer. Fig. 11 shows one out of 20 to demonstrate the performance of the algorithms.
CONCLUSIONS
An adaptive loudness management system has been proposed and simulated. System is made up of mainly two modules; audio acquisition and loudness measurement module that measures the loudness that human would hear and loudness control module that reduces the loudness level of the audio signals using gain control. The default threshold level used in this system is -23 LKFS but this value can be adjusted by the user according to their listening preferences. The loudness measurement algorithm complies with the standard BS 1770-1 that was developed by the ITU-R therefore it follows an international loudness metering standard. A window size of 2.5 seconds has been used for the measuring of loudness. This value also can be changed by the user in accordance to their preference. At the initial stage of the project, there was no standard window size that is to be used for this purpose. Based on extensive informal subjective tests that were carried out, the value of the window sized was chosen as 2.5 seconds. In the R128 standard that was published by EBU; it was recommended that the window size of 3 seconds were to be used for short term loudness measurement. This value is very close to the value that has been decided for this research prior to the publication of R128. REFERENCES [1]
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