Select the OCM.html file to run it now, or download the OCMdemo.zip file.
This article, Energy and Atmosphere, looks at the energy dynamics of the Earth's atmosphere. Since the role of radiative
gasses has become a political issue that is undermining the stability of industrial
economies and denying the many benefits of cheap and reliable energy to billions of people, the
precise nature of the energy dynamics of our atmosphere has become a trillion dollar question.
It shows a new derivation for the adiabatic temperature lapse rate in the atmosphere.
It also points to a possible explanation for why the Earth's water thermostat cuts in so suddenly at 30 Cº.
The IPCC and the Carbon Cycle
We are told by the IPCC that CO2 emissions from burning fossil fuels are causing atmospheric CO2 levels to rise and that these are causing global warming. Of the two links in this chain of reasoning this article addresses the first.
I show that the IPCC view of the carbon cycle is fundamentally flawed in many ways, and is not supportable at any meaningful level of confidence. This is not esoteric science to be left to specialists or ‘great minds’. Any numerate person who cares to look and think can understand the insignificance of our total industrial era CO2 emissions at less than 1% of the carbon cycle and our annual emissions at just 5% of the air-sea fluxes.
The article Radiative Delay in Context challenges a core assumption of the contemporary climate science consensus, that the Greenhouse (or Radiative Delay) Effect is the sole mechanism by which the Earth's atmosphere raises the Earth's surface temperature above that which would exist without an atmosphere.
In it I show that the Radiative Delay heating of the atmosphere is negligible, and a well established alternative mechanism arising from atmospheric buffering over the diurnal temperature cycle is capable of producing the temperatures we experience.
The role of Carbon Dioxide is shown to be negligible.
This is a personal view of the political nature of the CO2 scare from an environmentalist who has watched on in dismay at the extreme politicisation of the environment. I watched the takeover of the environment movement since the 1970s by the extreme left acting with motives that have nothing to do with reducing our environmental impact.
Moving forward we see the actions of the totalitarian left in the United Nations and associated NGOs forming the Intergovernmental Panel on Climate Change (IPCC), and how this has perverted the already corrupted nexus between science and public policy.
SLabView is a standalone Java application designed for visual exploration of speech signals.
It uses the same approach for analysing the signal as its sister package
It displays the raw speech signal and can also display a frequency domain spectrum of chunks of speech
along with a wide array of parameters that have been used in speech recognition systems.
Some of SLabView's features are:
• Signal view
for viewing the time domain speech signal
(spectral) view for viewing speech spectra
• Source Synchronous or fixed length signal framing
• Overlayed signal parameter plots (over 24 parameters)
• Point and click selection
of frames for specrum viewing
• Drag selection of multiple
frames for spectal animation
• Formant markers in the frequency view
• Saving text files of spectral data (disabled)
• Saving GIF files of the speech signal and spectra (local operation only)
• Saving Quicktime movies of spectral sequences (disabled)
Signal View 1 is an example
of the SLabView Signal View showing a full speech utterance in low resolution
Signal View 2 is an example of the SLabView Signal View showing a segment of a speech utterance in high resolution
Frame Frequencies 3
is an example of the SLabView Spectrum for the highlighted frame in Signal
An Unnecessary Distortion
Signal View 2 above can be seen as four repeated epochs marked by the vertical green lines (with the second episode highlighted). Each of these represents a single vibration of the glotis - the flaps of tissue in our throat that vibrate to produce voiced speech. The brain doesn't have a clock measuring absolute time at a millisecond scale. It operates with an episodic view of time and it readily recognises each glottal epoch as a distinct episode defining a time frame.
There is an error made at the very start of conventional speech analysis in the initial signal processing step - breaking an utterance into short frames for spectral analysis. Rather than using the natural framing, the standard approach is to chunk the signal into fixed length frames - typically ten milliseconds long as shown by the vertical purple lines in Signal View 2.
Doing this intruduces a distortion in the resulting spectrum that is illustrated in the two following images.
(4a) Spectrum Produced From Source Synchronous Framing
(4b) Spectrum Produced From Fixed Length Framing
The first is produced using glottal, or Source Synchronous, framing - the green dividing lines in View 2. It is smooth and regular from frame to frame. The second diagram shows the result of fixed length framing - the red dividions. It has a serrated artifact superimposed and varies more between frames that the synchronous results do.
The reason for the serration is theoretically trivial - the signal in the fixed frames (between purple lines in View 2) doesn't start and finish near zero values. Artificially forcing the ends to zero (as is usually done) helps a little but the distortion and irregularity persist. All subsequent analysis is unnecessarily compromised. Since the length of glottal epochs varies, fixed frames can never consistently capture a single epoch.
The SLab package uses Source Synchronous framing that allows it to easily determine the position of peaks in the spectrum. It can also treat each frame as a basic unit, rather than having to add a smoothing step to even out the artificial irregularities between adjacent frames. Timing is a crucial element in many of the subtle cues that help us recognise speech. Having a good grasp of timing is bound to be an important element of automating the process.
I was surprised to find that many ASR researchers assumed that 4b - all they had ever seen - was the real speech spectrum and insisted that I had just smoothed it, losing information in the process. The truth is easily demonstrated with SlabView. Changing the fixed, and arbitrary, sampling length used in 4b changes the separation of the narrow artifacts - just as signal theory says it should.
Download SLabView source code and Mac Application here and change the SLabView.html file as instructed to point to your user directory.