Preset (Everyday) + transform + exposure + graduated filter + radial filter. If shots like this take more than 2 minutes to edit, it’s probably not worth editing. ⏱
—
Boxing Day will be the last day to get my Lightroom presets discounted, which leaves you only 3 more days! Get on it! ???? (at Toronto, Ontario)

I just realized that I can export my entire story all at once now, which means uploading my tutorials to my Facebook page will be a million times easier (it was tedious to stitch all the individual clips together before). ????
.
Related: I posted a story this morning deconstructing the edit on yesterday’s shot.
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Also related: I uploaded the 3 tutorials from my November feature on @thecreatorclass to my Facebook page this morning too. More to come! (at London, United Kingdom)

Wow, this has been a busy period. I’m just back from the Ampersand web typography conference in Brighton, and having a catch-up day in Mild Bunch HQ. Just before that I’ve been working flat out. First on Mapalong which was a grass-roots sponsor of Ampersand, and is going great guns. Then on an article for The Manual which is being published soon, and on 8 Faces #3 which is in progress right now. Not to mention the new talk for Ampersand which left me scratching my head and wondering if I was making any sense at all. More on that in a subsequent post.

In the meantime two previous events deserve a mention. (This is me starting more of a journalistic blog. :)

First of all, an interview with Simon Pascal Klien, the typographer and designer who’s curating the Design Festival podcast at the moment. We talked about all things web typography. Pascal cheekily left in a bit of noise from me in the prelude, and that rant pretty much sets the tone for the rest of the conversation. Thanks for your time, Pascal! If anyone reading this would care to listen in, the podcast can be downloaded or played from here:

Secondly, Daniel Bogan of The Setup sent me a few questions about my own tools. My answers are pretty clipped because of time, but you may find it interesting to compare this designer’s setup with your own:

I should note that in the meantime I’ve started writing with Writer, and discovered the great joy of keeping a journal and notes with a Midori Traveler’s Notebook. The latter is part of an on-going search I’m having to find Tools for Life. More on that, too at some point. Here’s my current list of topics I want to write about shortly:

• Ampersand, the aftermath
• Marrying a FujiFilm X100
• No-www
• Tools for life
• Paper versus pixels

There, I’ve written it!

The new General Data Protection Regulation (GDPR) of the European Union will be implemented on May 25th, 2018. We used this opportunity to rework many of our internal data processing structures, removed unnecessary trackers and apply this strict and transparent regulation also to all our customers worldwide.

Image from pixapay.com.

At Auphonic we store as few personal information as possible about your usage and production data.
Here are a few human-readable excerpts from our privacy policy about which information we collect, how we process it, how long and where we store it - for more details please see our full Privacy Policy.

Information that we collect

• Your email address when you create an account.
• Your files, content, configuration parameters and other information, including your photos, audio or video files, production settings, metadata and emails.
• Your tokens or authentication information if you choose to connect to any External services.
• Your subscription plan, credits purchases and production billing history associated with your account, where applicable.
• Your interactions with us, whether by email, on our blog or on our social media platforms.

We do not process any special categories of data (also commonly referred to as “sensitive personal data”).

How we use and process your Data

• To authenticate you when you log on to your account.
• To run your Productions, such that Auphonic can create new media files from your Content according to your instructions.
• To improve our audio processing algorithms. For this purpose, you agree that your Content may be viewed and/or listened to by an Auphonic employee or any person contracted by Auphonic to work on our audio processing algorithms.
• To connect your Auphonic account to an External service according to your instructions.
• To develop, improve and optimize the contents, screen layouts and features of our Services.
• To follow up on any question and request for assistance or information.

When using our Service, you fully retain any rights that you have with regards to your Content, including copyright.

How long we store your Information

Your Productions and any associated audio or video files will be permanently deleted from our servers including all its metadata and possible data from external services after 21 days (7 days for video productions).
We will, however, keep billing metadata associated with your Productions in an internal database (how many hours of audio you processed).

Also, we might store selected audio and/or video files (or excerpts thereof) from your Content in an internal storage space for the purpose of improving our audio processing algorithms.

Other information like Presets, connected External services, Account settings etc. will be stored until you delete them or when your account is deleted.

Where we store your Data

All data that we collect from you is stored on secure servers in the European Economic Area (in Germany).

More Information and Contact

For more information please read our full Privacy Policy.

Please do not hesitate to contact us regarding any matter relating to our privacy policy and GDPR compliance!

In a previous blogpost we talked about the Opus codec, which offers very low bitrates. Another codec seeking to achieve even lower bitrates is Codec 2.

Codec 2 is designed for use with speech only, and although the bitrates are impressive the results aren’t as clear as Opus, as you can hear in the following audio examples. However, there is some interesting work being done with Codec 2 in combination with neural network (WaveNets) that is yielding great results.

Layers of a WaveNet neural network.

Background

Codec 2 is an open source codec designed for speech, and aims for compression rates between 700bps and 3200bps (bits per seconds).

The man behind it, David Rowe, is an electronic engineer currently living in South Australia. He started the project in September 2009, with the main aim of improving low-cost radio communication for people living in remote areas of the world. With this in mind, he set out to develop a codec that would significantly reduce file sizes and the bandwidth required when streaming.

Another motivation according to David, was to be free from patented technologies used by closed source codes which he believes “require expensive and awkward licenses and are stifling innovation”. His belief is that this work can be done without requiring the use of patent protected codecs, so all his work is open source.

Potential Applications

Rowe’s perceived applications include VOIP trunking, voice over low bandwidth HF/VHF digital radio, (especially for amateur radio, so as to avoid issues with the use of proprietary codecs), and developing world and remote area communications, including military, police and emergency services.

Why we’re interested here at Auphonic is for its potential for longer podcasts, presentations and audiobooks, allowing for low storage and minimizing the effect of bad network connections.

How it Works

To achieve the lower rates sought, speech has to be reduced into the smallest possible information/data, and this means that the amount of redundant information that is transmitted has to be minimized.

To do this, Codec 2 uses harmonic sinusoidal speech coding. This splits the speech into 10 - 30ms segments, called frames. Each frame is then analysed for the fundamental frequency (or pitch), and the number of harmonics that fit into a 4Khz bandwidth. Further, for each of the harmonics within the 4khz range, the amplitude and phase are recorded.

This information is then coded, and the decoder reconstructs the audio based on this data.

Codec 2 Block diagrams - Encoder (left) And decoder (right)
Figure from Rowtel.

Audio Examples and Comparison with other Codecs

Whilst it all sounds great in theory, how does the reality match up? Let’s have a listen.

Here is a short wav audio file:

intro-orig.wav - 1.3 MB (download):

Applying Codec 2 (without the WaveNet decoder) at the different rates available, 3200bps, 2400bps,1600bps,1200bps and 700bps, we get:

3200bps (download):

2400bps (download):

1600bps (download):

1200bps (download):

700bps (download):

These examples show significantly reduced file sizes.
Putting that information more meaningfully in terms of how much storage you would need for an hour of audio:

• At 3200bps, 1 hour of audio requires only 1.37MB (this would fit on one old 3½-inch floppy disk!)
• A rate of 2400bps equates to 1.03MB/h
• A rate of 1600bps equates to 0.68MB/h (Or approximately 2 hours of audio on one floppy disk!)
• A rate of 1200bps equates to 0.51MB/h
• A rate of 700bps equates to 0.3MB/h

So great compression, but the result is clearly not natural sounding.

As a comparison here is the same audio as a 8kb/s MP3:

MP3 at 8 kb/s - 23kb file size (download):

The file size is significantly larger than Codec 2 and the quality is arguably still not useable. You can clearly hear what is sometimes called sizzle - the weird metallic sounds you hear on low quality MP3s.

There is a final codec which is worth comparing, one that that seems to capture the two ideals of usable quality at low bitrates that we want: Opus.
Because of it's convincing low-bitrate performance, Auphonic already offers Opus encoding all the way down to 6 kbps, the lowest bitrate that Opus supports.

Comparing Opus at this 6 kbps rate to the 8kbps MP3 shows a significant improvement - although slightly muffled, it still sounds natural:

Opus at 6kbps (download):

Returning to Codec 2, and purely as s a bit of fun, here are some samples of Codec 2 on music! (Note that Codec 2 is not designed for music, it was only ever conceived for use on speech).

Original file (download):

As a 8kbps MP3 (download):

I personally couldn’t listen to the MP3 at this rate, so let’s listen to what Codec 2 does!

Codec 2 at different bitrates:

3200bps (download):

2400bps (download):

1600bps (download):

1200bps (download):

700bps (download):

As you can hear, it is not suitable for this application at all!

Codec 2 and WaveNet

As we have heard, despite the impressive bitrates achieved, the end result is not very natural sounding.
However, where it starts to get more interesting is the work done by W. Bastiaan Kleijn from Cornell University Library. He has been using with Codec 2 running at 2400bps on the coding side, but replaced the Codec 2 decoder with a WaveNet deep learning generative model (for more informationsee the paper Wavenet based low rate speech coding).

Here are some samples from the authors:

Codec	Male Example
Original File
Codec 2
With WaveNet Decoder

Codec	Female Example
Original File
Codec 2
With WaveNet Decoder

Comparing to Codec 2 you can hear a significant increase in quality, and if you compare to the original, there is not a significant decrease in quality.

David Rowe himself has stated that he considers the result to be "a game changer for low bit rate speech coding" and “as good an an 8000bps wideband speech codec”.

Conclusion

Whilst the (original) Codec 2 project represents very interesting work, it is limited, and the end result is not suited for podcasting. Also as we heard in the audio examples, it can only be used for voice recordings, and not music.

However, Codec 2 in combination with a WaveNet decoder improves the quality a lot and the low bitrate (2400bps) would be extremely interesting for podcasts and audiobooks distribution as well: one hour of audio would require only 1.03MB of storage!

Auphonic will add support for Codec 2 output files when the WaveNet decoder is in a usable form. For now we have just added support for Codec 2 input files.

If listeners find themselves using the volume up and down buttons a lot, level differences within your podcast or audio file are too big.
In this article, we are discussing why audio dynamic range processing (or leveling) is more important than loudness normalization, why it depends on factors like the listening environment and the individual character of the content, and why the loudness range descriptor (LRA) is only reliable for speech programs.

Photo by Alexey Ruban.

Why loudness normalization is not enough

Everybody who has lived in an apartment building knows the problem: you want to enjoy a movie late at night, but you're constantly on the edge - not only because of the thrilling story, but because your index finger is hovering over the volume down button of your remote. The next loud sound effect is going to come sooner rather than later, and you want to avoid waking up your neighbors with some gunshot sounds blasting from your TV.

In our previous post, we talked about the overall loudness of a production. While that's certainly important to keep in mind, the loudness target is only an average value, ignoring how much the loudness varies within a production. The loudness target of your movie might be in the ideal range, yet the level differences between a gunshot and someone whispering can still be enormous - having you turn the volume down for the former and up for the latter.

While the average loudness might be perfect, level differences can lead to an unpleasant listening experience.

Of course, this doesn't apply to movies alone. The image above shows a podcast or radio production. The loud section is music, the very quiet section just breathing, and the remaining sections are different voices.

To be clear, we're not saying that the above example is problematic per se. There are many situations, where a big difference in levels - a high dynamic range - is justified: for instance, in a movie theater, optimized for listening and without any outside noise, or in classical music.
Also, if the dynamic range is too small, listening can be tiring.

But if you watch the same movie in an outdoor screening in the summer on a beach next to the crashing waves or in the middle of a noisy city, it can be tricky to hear the softer parts.
Spoken word usually has a smaller dynamic range, and if you produce your podcast for a target audience of train or car commuters, the dynamic range should be even smaller, adjusting for the listening situation.

Therefore, hitting the loudness target has less impact on the listening experience than level differences (dynamic range) within one file!
What makes a suitable dynamic range does not only depend on the listening environment, but also on the nature of the content itself. If the dynamic range is too small, the audio can be tiring to listen to, whereas more variability in levels can make a program more interesting, but might not work in all environments, such as a noisy car.

Dynamic range experiment in a car

Wolfgang Rein, audio technician at SWR, a public broadcaster in Germany, did an experiment to test how drivers react to programs with different dynamic ranges. They monitored to what level drivers set the car stereo depending on speed (thus noise level) and audio dynamic range.
While the results are preliminary, it seems like drivers set the volume as low as possible so that they can still understand the content, but don't get distracted by loud sounds.

As drivers adjust the volume to the loudest voice in a program, they won't understand quieter speakers in content with a high dynamic range anymore. To some degree and for short periods of time, they can compensate by focusing more on the radio program, but over time that's tiring. Therefore, if the loudness varies too much, drivers tend to switch to another program rather than adjusting the volume.
Similar results have been found in a study conducted by NPR Labs and Towson University.

On the other hand, the perception was different in pure music programs. When drivers set the volume according to louder parts, they weren't able to hear softer segments or the beginning of a song very well. But that did not matter to them as much and didn't make them want to turn up the volume or switch the program.

Listener's reaction in response to frequent loudness changes. (from John Kean, Eli Johnson, Dr. Ellyn Sheffield: Study of Audio Loudness Range for Consumers in Various Listening Modes and Ambient Noise Levels)

Loudness comfort zone

The reaction of drivers to variable loudness hints at something that BBC sound engineer Mike Thornton calls the loudness comfort zone.

Tests (...) have shown that if the short-term loudness stays within the "comfort zone" then the consumer doesn’t feel the need to reach for the remote control to adjust the volume.

In a blog post, he highlights how the series Blue Planet 2 and Planet Earth 2 might not always have been the easiest to listen to. The graph below shows an excerpt with very loud music, followed by commentary just at the bottom of the green comfort zone. Thornton writes: "with the volume set at a level that was comfortable when the music was playing we couldn’t always hear the excellent commentary from Sir David Attenborough and had to resort to turning on the subtitles to be sure we knew what Sir David was saying!"

Planet Earth 2 Loudness Plot Excerpt. Colored green: comfort zone of +3 to -5LU around the loudness target. (from Mike Thornton: BBC Blue Planet 2 Latest Show In Firing Line For Sound Issues - Are They Right?)

As already mentioned above, a good mix considers the maximum and minimum possible loudness in the target listening environment.
In a movie theater the loudness comfort zone is big (loudness can vary a lot), and loud music is part of the fun, while quiet scenes work just as well. The opposite was true in the aforementioned experiment with drivers, where the loudness comfort zone is much smaller and quiet voices are difficult to understand.

Hence, the loudness comfort zone determines how much dynamic range an audio signal can use in a specific listening environment.

How to measure dynamic range: LRA

When producing audio for various environments, it would be great to have a target value for dynamic range, (the difference between the smallest and largest signal values of an audio signal) as well. Then you could just set a dynamic range target, similarly to a loudness target.

Theoretically, the maximum possible dynamic range of a production is defined by the bit-depth of the audio format. A 16-bit recording can have a dynamic range of 96 dB; for 24-bit, it's 144 dB - which is well above the approx. 120 dB the human ear can handle. However, most of those bits are typically being used to get to a reasonable base volume. Picture a glass of water: you want it to be almost full, with some headroom so that it doesn't spill when there's a sudden movement, i.e. a bigger amplitude wave at the top.

Determining the dynamic range of a production is easier said than done, though. It depends on which signals are included in the measurement: for example, if something like background music or breathing should be considered at all.
The currently preferred method for broadcasting is called Loudness Range, LRA. It is measured in Loudness Units (LU), and takes into account everything between the 10th and the 95th percentile of a loudness distribution, after an additional gating method. In other words, the loudest 5% and quietest 10% of the audio signal are being ignored. This way, quiet breathing or an occasional loud sound effect won't affect the measurement.

Loudness distribution and LRA for the film 'The Matrix'. Figure from EBU Tech Doc 3343 (p.13).

However, the main difficulty is which signals should be included in the loudness range measurement and which ones should be gated. This is unfortunately often very subjective and difficult to define with a purely statistical method like LRA.

Where LRA falls short

Therefore, only pure speech programs give reliable LRA values that are comparable!
For instance, a typical LRA for news programs is 3 LU; for talks and discussions 5 LU is common. LRA values for features, radio dramas, movies or music very much depend on the individual character and might be in the range between 5 and 25 LU.

To further illustrate this, here are some typical LRA values, according to a paper by Thomas Lund (table 2):

Program	Loudness Range
Matrix, full movie	25.0
NBC Interstitials, Jan. 2008, all together (3:30)	9.4
Friends Episode 16	6.6
Speak Ref., Male, German, SQUAM Trk 54	6.2
Speak Ref., Female, French, SQUAM Trk 51	4.8
Speak Ref., Male, English, Sound Check	3.3
Wish You Were Here, Pink Floyd	22.1
Gilgamesh, Battle of Titans, Osaka Symph.	19.7
Don’t Cry For Me Arg., Sinead O’Conner	13.7
Beethoven Son in F, Op17, Kliegel & Tichman	12.0
Rock’n Roll Train, AC/DC	6.0
I.G.Y., Donald Fagen	3.6

LRA values of music are very unpredictable as well.
For instance, Tom Frampton measured the LRA of songs in multiple genres, and the differences within each genre are quite big. The ten pop songs that he analyzed varied in LRA between 3.7 and 12 LU, country songs between 3.6 and 14.9 LU. In the Electronic genre the individual LRAs were between 3.7 and 15.2 LU. Please see the tables at the bottom of his blog post for more details.

We at Auphonic also tried to base our Adaptive Leveler parameters on the LRA descriptor. Although it worked, it turned out that it is very difficult to set a loudness range target for diverse audio content, which does include speech, background sounds, music parts, etc. The results were not predictable and it was hard to find good target values. Therefore we developed our own algorithm to measure the dynamic range of audio signals.

In conclusion, LRA comparisons are only useful for productions with spoken word only and the LRA value is therefore not applicable as a general dynamic range target value. The more complex a production gets, the more difficult it is to make any judgment based on the LRA.
This is, because the definition of LRA is purely statistical. There's no smart measurement using classifiers that distinguish between music, speech, quiet breathing, background noises and other types of audio. One would need a more intelligent algorithm (as we use in our Adaptive Leveler), that knows which audio segments should be included and excluded from the measurement.

From theory to application: tools

Loudness and dynamic range clearly is a complicated topic. Luckily, there are tools that can help. To keep short-term loudness in range, a compressor can help control sudden changes in loudness - such as p-pops or consonants like t or k. To achieve a good mid-term loudness, i.e. a signal that doesn't go outside the comfort zone too much, a leveler is a good option. Or, just use a fader or manually adjust volume curves. And to make sure that separate productions sound consistent, loudness normalization is the way to go. We have covered all of this in-depth before.

Looking at the audio from above again, with an adaptive leveler applied it looks like this:

Leveler example. Output at the top, input with leveler envelope at the bottom.

Now, the voices are evened out and the music is at a comfortable level, while the breathing has not been touched at all.
We recently extended Auphonic's adaptive leveler, so that it is now possible to customize the dynamic range - please see adaptive leveler customization and advanced multitrack audio algorithms.
If you wanted to increase the loudness comfort zone (or dynamic range) of the standard preset by 10 dB (or LU), for example, the envelope would look like this:

Leveler with higher dynamic range, only touching sections with extremely low or extremely high loudness to fit into a specific loudness comfort zone.

When a production is done, our adaptive leveler uses classifiers to also calculate the integrated loudness and loudness range of dialog and music sections separately. This way it is possible to just compare the dialog LRA and loudness of complex productions.

Assessing the LRA and loudness of dialog and music separately.

Conclusion

Getting audio dynamics right is not easy. Yet, it is an important thing to keep in mind, because focusing on loudness normalization alone is not enough. In fact, hitting the loudness target often has less impact on the listening experience than level differences, i.e. audio dynamics.

If the dynamic range is too small, the audio can be tiring to listen to, whereas a bigger dynamic range can make a program more interesting, but might not work in loud environments, such as a noisy train.
Therefore, a good mix adapts the audio dynamic range according to the target listening environment (different loudness comfort zones in cinema, at home, in a car) and according to the nature of the content (radio feature, movie, podcast, music, etc.).

Furthermore, because the definition of the loudness range / LRA is purely statistical, only speech programs give reliable LRA values that are comparable.
More "intelligent" algorithms are in development, which use classifiers to decide which signals should be included and excluded from the dynamic range measurement.

If you understand German, take a look at our presentation about audio dynamic processing in podcasts for further information:

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The post How to Create Spotify Colorizer Effects With CSS Blend Modes appeared first on Web Designer Wall.

There are hundreds of ways to create responsive navigation, limited only by your creativity and the boundaries of what CSS can accomplish. Good responsive navigation is a little harder – a responsive menu must become a mobile menu, adhering to the needs and rules of touch-driven devices. Mobile design is rapidly changing, and so the […]

The post Four Elements of Truly Mobile-Friendly Responsive Menus appeared first on Web Designer Wall.

UPDATE: After spending an extensive amount of time with the HTC One we have added our thoughts regarding the handset’s battery life. Click here to find out what we reckon. The HTC One is the latest Android handset from the Taiwanese manufacturer and is aimed firmly at the flagship end of the market, featuring a … Continue reading HTC One Review: Top Of The Premium Pops? →

By Lauren McCauley Common Dreams At this rate, plastics production will account for 20 percent of total oil consumption and 15 percent of the global annual carbon budget by 2050. The weight of plastic waste clogging the world’s oceans threatens … Continue reading →

By Hannah Hickey UWNEWS While winter sea ice in the Arctic is declining so dramatically that ships can now navigate those waters without any icebreaker escort, the scene in the Southern Hemisphere is very different. Sea ice area around Antarctica … Continue reading →

Learn how to sell strategy... Jacob Cass & Matt Davies give different perspectives on how to approach sales & the brand building process. Tune in to episode 4!

Do you know the difference between a mission and a vision? Or the difference between a purpose and a goal? Don’t worry, you’re not the only one. Let's clarify!

A brand strategy is a long-term plan that affects every facet of your business, but creating one can be confusing. We break it down into 7 essential components.

Online representation has a crucial role in planning a business. Today, people turn to the internet whenever they need help, but especially when they want to find certain products or specific...