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Podtrac Releases Ranker Of Top 20 Podcast Publishers For April

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Police release image of man who may be able help North Belfast burglary probe

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Edison Research, NPR Release 2020 Smart Audio Report

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Bob Dylan To Release First Album Of New Material In Eight Years

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Exponential Infection Increases Are Deadly Serious

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-- Read more on ScientificAmerican.com




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People living with HIV face premature heart disease and barriers to care

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WordPress Theme Releases for 3/11

Attitude is a simple, clean, and responsive retina-ready theme. Catch Everest is a simple, clean, and responsive theme. Liberus is an ideal business related theme that is relatively simple and would suit any blog or website. Stitch is an elegant, modern theme with optional fixed header.




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WordPress Plugin Releases for 3/13

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WordPress Theme Releases for 3/15

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WordPress Theme Releases for 3/19

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WordPress Theme Releases for 3/23

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WordPress Plugin Releases for 3/25

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WordPress Theme Releases for 3/28

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WordPress Plugin Releases for 3/30

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Search Unleased: A custom WordPress plugin

Most all WordPress theme’s use a a simple search form to search your site. But what it you wanted to search your whole site and not just your posts. Search Unleashed comes into the picture. Search Unleashed performs searches across all data, including that added by plugins. Some features of this plugin are: Full text […]

The post Search Unleased: A custom WordPress plugin appeared first on WPCult.




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Auphonic Audio Inspector Release

At the Subscribe 9 Conference, we presented the first version of our new Audio Inspector:
The Auphonic Audio Inspector is shown on the status page of a finished production and displays details about what our algorithms are changing in audio files.

A screenshot of the Auphonic Audio Inspector on the status page of a finished Multitrack Production.
Please click on the screenshot to see it in full resolution!

It is possible to zoom and scroll within audio waveforms and the Audio Inspector might be used to manually check production result and input files.

In this blog post, we will discuss the usage and all current visualizations of the Inspector.
If you just want to try the Auphonic Audio Inspector yourself, take a look at this Multitrack Audio Inspector Example.

Inspector Usage

Control bar of the Audio Inspector with scrollbar, play button, current playback position and length, button to show input audio file(s), zoom in/out, toggle legend and a button to switch to fullscreen mode.

Seek in Audio Files
Click or tap inside the waveform to seek in files. The red playhead will show the current audio position.
Zoom In/Out
Use the zoom buttons ([+] and [-]), the mouse wheel or zoom gestures on touch devices to zoom in/out the audio waveform.
Scroll Waveforms
If zoomed in, use the scrollbar or drag the audio waveform directly (with your mouse or on touch devices).
Show Legend
Click the [?] button to show or hide the Legend, which describes details about the visualizations of the audio waveform.
Show Stats
Use the Show Stats link to display Audio Processing Statistics of a production.
Show Input Track(s)
Click Show Input to show or hide input track(s) of a production: now you can see and listen to input and output files for a detailed comparison. Please click directly on the waveform to switch/unmute a track - muted tracks are grayed out slightly:

Showing four input tracks and the Auphonic output of a multitrack production.

Please click on the fullscreen button (bottom right) to switch to fullscreen mode.
Now the audio tracks use all available screen space to see all waveform details:

A multitrack production with output and all input tracks in fullscreen mode.
Please click on the screenshot to see it in full resolution.

In fullscreen mode, it’s also possible to control playback and zooming with keyboard shortcuts:
Press [Space] to start/pause playback, use [+] to zoom in and [-] to zoom out.

Singletrack Algorithms Inspector

First, we discuss the analysis data of our Singletrack Post Production Algorithms.

The audio levels of output and input files, measured according to the ITU-R BS.1770 specification, are displayed directly as the audio waveform. Click on Show Input to see the input and output file. Only one file is played at a time, click directly on the Input or Output track to unmute a file for playback:

Singletrack Production with opened input file.
See the first Leveler Audio Example to try the audio inspector yourself.

Waveform Segments: Music and Speech (gold, blue)
Music/Speech segments are displayed directly in the audio waveform: Music segments are plotted in gold/yellow, speech segments in blue (or light/dark blue).
Waveform Segments: Leveler High/No Amplification (dark, light blue)
Speech segments can be displayed in normal, dark or light blue: Dark blue means that the input signal was very quiet and contains speech, therefore the Adaptive Leveler has to use a high amplification value in this segment.
In light blue regions, the input signal was very quiet as well, but our classifiers decided that the signal should not be amplified (breathing, noise, background sounds, etc.).

Yellow/orange background segments display leveler fades.

Background Segments: Leveler Fade Up/Down (yellow, orange)
If the volume of an input file changes in a fast way, the Adaptive Leveler volume curve will increase/decrease very fast as well (= fade) and should be placed in speech pauses. Otherwise, if fades are too slow or during active speech, one will hear pumping speech artifacts.
Exact fade regions are plotted as yellow (fade up, volume increase) and orange (fade down, volume decrease) background segments in the audio inspector.

Horizontal red lines display noise and hum reduction profiles.

Horizontal Lines: Noise and Hum Reduction Profiles (red)
Our Noise and Hiss Reduction and Hum Reduction algorithms segment the audio file in regions with different background noise characteristics, which are displayed as red horizontal lines in the audio inspector (top lines for noise reduction, bottom lines for hum reduction).
Then a noise print is extracted in each region and a classifier decides if and how much noise reduction is necessary - this is plotted as a value in dB below the top red line.
The hum base frequency (50Hz or 60Hz) and the strength of all its partials is also classified in each region, the value in Hz above the bottom red line indicates the base frequency and whether hum reduction is necessary or not (no red line).

You can try the singletrack audio inspector yourself with our Leveler, Noise Reduction and Hum Reduction audio examples.

Multitrack Algorithms Inspector

If our Multitrack Post Production Algorithms are used, additional analysis data is shown in the audio inspector.

The audio levels of the output and all input tracks are measured according to the ITU-R BS.1770 specification and are displayed directly as the audio waveform. Click on Show Input to see all the input files with track labels and the output file. Only one file is played at a time, click directly into the track to unmute a file for playback:

Input Tracks: Waveform Segments, Background Segments and Horizontal Lines
Input tracks are displayed below the output file including their track names. The same data as in our Singletrack Algorithms Inspector is calculated and plotted separately in each input track:
Output Waveform Segments: Multiple Speakers and Music
Each speaker is plotted in a separate, blue-like color - in the example above we have 3 speakers (normal, light and dark blue) and you can see directly in the waveform when and which speaker is active.
Audio from music input tracks are always plotted in gold/yellow in the output waveform, please try to not mix music and speech parts in music tracks (see also Multitrack Best Practice)!

You can try the multitrack audio inspector yourself with our Multitrack Audio Inspector Example or our general Multitrack Audio Examples.

Ducking, Background and Foreground Segments

Music tracks can be set to Ducking, Foreground, Background or Auto - for more details please see Automatic Ducking, Foreground and Background Tracks.

Ducking Segments (light, dark orange)
In Ducking, the level of a music track is reduced if one of the speakers is active, which is plotted as a dark orange background segment in the output track.
Foreground music parts, where no speaker is active and the music track volume is not reduced, are displayed as light orange background segments in the output track.
Background Music Segments (dark orange background)
Here the whole music track is set to Background and won’t be amplified when speakers are inactive.
Background music parts are plotted as dark organge background segments in the output track.
Foreground Music Segments (light orange background)
Here the whole music track is set to Foreground and its level won’t be reduced when speakers are active.
Foreground music parts are plotted as light organge background segments in the output track.

You can try the ducking/background/foreground audio inspector yourself: Fore/Background/Ducking Audio Examples.

Audio Search, Chapters Marks and Video

Audio Search and Transcriptions
If our Automatic Speech Recognition Integration is used, a time-aligned transcription text will be shown above the waveform. You can use the search field to search and seek directly in the audio file.
See our Speech Recognition Audio Examples to try it yourself.
Chapters Marks
Chapter Mark start times are displayed in the audio waveform as black vertical lines.
The current chapter title is written above the waveform - see “This is Chapter 2” in the screenshot above.

A video production with output waveform, input waveform and transcriptions in fullscreen mode.
Please click on the screenshot to see it in full resolution.

Video Display
If you add a Video Format or Audiogram Output File to your production, the audio inspector will also show a separate video track in addition to the audio output and input tracks. The video playback will be synced to the audio of output and input tracks.

Supported Audio Formats

We use the native HTML5 audio element for playback and the aurora.js javascript audio decoders to support all common audio formats:

WAV, MP3, AAC/M4A and Opus
These formats are supported in all major browsers: Firefox, Chrome, Safari, Edge, iOS Safari and Chrome for Android.
FLAC
FLAC is supported in Firefox, Chrome, Edge and Chrome for Android - see FLAC audio format.
In Safari and iOS Safari, we use aurora.js to directly decode FLAC files in javascript, which works but uses much more CPU compared to native decoding!
ALAC
ALAC is not supported by any browser so far, therefore we use aurora.js to directly decode ALAC files in javascript. This works but uses much more CPU compared to native decoding!
Ogg Vorbis
Only supported by Firefox, Chrome and Chrome for Android - for details please see Ogg Vorbis audio format.

We suggest to use a recent Firefox or Chrome browser for best performance.
Decoding FLAC and ALAC files also works in Safari and iOS with the help of aurora.js, but javascript decoders need a lot of CPU and they sometimes have problems with exact scrolling and seeking.

Please see our blog post Audio File Formats and Bitrates for Podcasts for more details about audio formats.

Mobile Audio Inspector

Multiple responsive layouts were created to optimize the screen space usage on Android and iOS devices, so that the audio inspector is fully usable on mobile devices as well: tap into the waveform to set the playhead location, scroll horizontally to scroll waveforms, scroll vertically to scroll between tracks, use zoom gestures to zoom in/out, etc.

Unfortunately the fullscreen mode is not available on iOS devices (thanks to Apple), but it works on Android and is a really great way to inspect everything using all the available screen space:

Audio inspector in horizontal fullscreen mode on Android.

Conclusion

Try the Auphonic Audio Inspector yourself: take a look at our Audio Example Page or play with the Multitrack Audio Inspector Example.

The Audio Inspector will be shown in all productions which are created in our Web Service.
It might be used to manually check production result/input files and to send us detailed feedback about audio processing results.

Please let us know if you have some feedback or questions - more visualizations will be added in future!







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Advanced Multitrack Audio Algorithms Release (Beta)

Last weekend, at the Subscribe10 conference, we released Advanced Audio Algorithm Parameters for Multitrack Productions:

We launched our advanced audio algorithm parameters for Singletrack Productions last year. Now these settings (and more) are available for Multitrack Algorithms as well, which gives you detailed control for each track of your production.

The following new parameters are available:

Please join our private beta program and let us know how you use these new features or if you need even more control!

Fore/Background Settings

The parameter Fore/Background controls whether a track should be in foreground, in background, ducked, or unchanged, which is especially important for music or clip tracks.
For more details, please see Automatic Ducking, Foreground and Background Tracks .

We now added the new option Unchanged and a new parameter to set the level of background segments/tracks:
Unchanged (Foreground):
We sometimes received complaints from users, which produced very complex music or clip tracks, that Auphonic changes the levels too hard.
If you set the parameter Fore/Background to the new option Unchanged (Foreground), Level relations within this track won’t be changed at all. It will be added to the final mixdown so that foreground/solo parts of this track will be as loud as (foreground) speech from other tracks.
Background Level:
It is now possible to set the level of background segments/tracks (compared to foreground segments) in background and ducking tracks. By default, background and ducking segments are 18dB softer than foreground segments.

Leveler Parameters

Similar to our Singletrack Advanced Leveler Parameters (see this previous blog post), we also released leveling parameters for Multitrack Productions now.
The following advanced parameters for our Multitrack Adaptive Leveler can be set for each track and allow you to customize which parts of the audio should be leveled, how much they should be leveled, how much dynamic range compression should be applied and to set the stereo panorama (balance):

Leveler Preset:
Select the Speech or Music Leveler for this track.
If set to Automatic (default), a classifier will decide if this is a music or speech track.
Dynamic Range:
The parameter Dynamic Range controls how much leveling is applied: Higher values result in more dynamic output audio files (less leveling). If you want to increase the dynamic range by 3dB (or LU), just increase the Dynamic Range parameter by 3dB.
For more details, please see Multitrack Leveler Parameters.
Compressor:
Select a preset for Micro-Dynamics Compression: Auto, Soft, Medium, Hard or Off.
The Compressor adjusts short-term dynamics, whereas the Leveler adjusts mid-term level differences.
For more details, please see Multitrack Leveler Parameters.
Stereo Panorama (Balance):
Change the stereo panorama (balance for stereo input files) of the current track.
Possible values: L100, L75, L50, L25, Center, R25, R50, R75 and R100.

If you understand German and want to know more about our Advanced Leveler Parameters and audio dynamics in general, watch our talk at the Subscribe10 conference:
Video: Audio Lautheit und Dynamik.

Better Hum and Noise Reduction Controls

We now offer three parameters to control the combination of our Multitrack Noise and Hum Reduction Algorithms for each input track:
Noise Reduction Amount:
Maximum noise and hum reduction amount in dB, higher values remove more noise.
In Auto mode, a classifier decides if and how much noise reduction is necessary (to avoid artifacts). Set to a custom (non-Auto) value if you prefer more noise reduction or want to bypass our classifier.
Hum Base Frequency:
Set the hum base frequency to 50Hz or 60Hz (if you know it), or use Auto to automatically detect the hum base frequency in each speech region.
Hum Reduction Amount:
Maximum hum reduction amount in dB, higher values remove more noise.
In Auto mode, a classifier decides how much hum reduction is necessary in each speech region. Set it to a custom value (> 0), if you prefer more hum reduction or want to bypass our classifier. Use Disable Dehum to disable hum reduction and use our noise reduction algorithms only.

Behavior of noise and hum reduction parameter combinations:

Noise Reduction Amount Hum Base Frequency Hum Reduction Amount
Auto Auto Auto Automatic hum and noise reduction
Auto or > 0 * Disabled No hum reduction, only denoise
Disabled 50Hz Auto or > 0 Force 50Hz hum reduction, no denoise
Disabled Auto Auto or > 0 Automatic dehum, no denoise
12dB 60Hz Auto or > 0 Always do dehum (60Hz) and denoise (12dB)

Maximum True Peak Level

In the Master Algorithm Settings of your multitrack production, you can set the maximum allowed true peak level of the processed output file, which is controlled by the True Peak Limiter after our Loudness Normalization algorithms.

If set to Auto (which is the current default), a reasonable value according to the selected loudness target is used: -1dBTP for 23 LUFS (EBU R128) and higher, -2dBTP for -24 LUFS (ATSC A/85) and lower loudness targets.

Full API Support

All advanced algorithm parameters, for Singletrack and Multitrack Productions, are available in our API as well, which allows you to integrate them into your scripts, external workflows and third-party applications.

Singletrack API:
Documentation on how to use the advanced algorithm parameters in our singletrack production API: Advanced Algorithm Parameters
Multitrack API:
Documentation of advanced settings for each track of a multitrack production:
Multitrack Advanced Audio Algorithm Settings

Join the Beta and Send Feedback

Please join our beta and let us know your case studies, if you need any other algorithm parameters or if you have any questions!

Here are some private beta invitation codes:

8tZPc3T9pH VAvO8VsDg9 0TwKXBW4Ni kjXJMivtZ1 J9APmAAYjT Zwm6HabuFw HNK5gF8FR5 Do1MPHUyPW CTk45VbV4t xYOzDkEnWP
9XE4dZ0FxD 0Sl3PxDRho uSoRQxmKPx TCI62OjEYu 6EQaPYs7v4 reIJVOwIr8 7hPJqZmWfw kti3m5KbNE GoM2nF0AcN xHCbDC37O5
6PabLBRm9P j2SoI8peiY olQ2vsmnfV fqfxX4mWLO OozsiA8DWo weJw0PXDky VTnOfOiL6l B6HRr6gil0 so0AvM1Ryy NpPYsInFqm
oFeQPLwG0k HmCOkyaX9R G7DR5Sc9Kv MeQLSUCkge xCSvPTrTgl jyQKG3BWWA HCzWRxSrgW xP15hYKEDl 241gK62TrO Q56DHjT3r4
9TqWVZHZLE aWFMSWcuX8 x6FR5OTL43 Xf6tRpyP4S tDGbOUngU0 5BkOF2I264 cccHS0KveO dT29cF75gG 2ySWlYp1kp iJWPhpAimF
We are happy to send further invitation codes to all interested users - please do not hesitate to contact us!

If you have an invitation code, you can enter it here to activate the Multitrack Advanced Audio Algorithm Parameters:
Auphonic Algorithm Parameters Private Beta Activation







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Released: Premium BlogStarter Theme

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Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning. (arXiv:2005.03227v1 [eess.IV])

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.




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SOUND ADVICE: New local music releases you can listen to right now

The future of live music might still be a big question mark, but local musicians aren't merely sitting on their hands during quarantine: A lot of them are finding a way to connect with their fans and putting out new material, even without the benefit of physical congregation. Here's a batch of some of our favorite new releases from the past month, and there will no doubt be more in the coming weeks.…




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How climate change is contributing to skyrocketing rates of infectious disease

A catastrophic loss in biodiversity, reckless destruction of wildland and warming temperatures have allowed disease to explode. Ignoring the connection between climate change and pandemics would be “dangerous delusion,” one scientist said. The scientists who study how diseases emerge in a changing environment knew this moment was coming.…



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Using cavitation to increase oil separation

Methods and systems are provided that apply cavitation to a grain-based liquid medium processing stream of an oil separation process in order to achieve increased yields. Ultrasonic sources can be used in generating the cavitation. Typically, the oil processing system is a downstream process of an alcohol (such as ethanol) production facility utilizing a dry grind, a modified dry grind or a wet mill alcohol production process.




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Germanium bridged metallocenes producing polymers with increased melt strength

This invention relates to a process for polymerizing ethylene comprising contacting ethylene and optional comonomers with a catalyst system comprising an activator and a transition metal compound represented by the formula: ##STR1## Wherein R1 and R2 are independently hydrogen or a group having up to 100 carbon atoms, Cp1 is a bulky ligand; Cp2 is a bulky ligand or a heteroatom optionally bound to a C1 to C50 hydrocarbyl group, n is the valence state of the transition metal, Tm is a Group 3 to 10 metal, and each X is independently an anionic leaving group.