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The message from Autodesk and the many companies that presented and exhibited at Autodesk University (AU) last month in San Diego was clear: for the product design and manufacturing (D&M), architecture, engineering, construction and operations (AECO), and media and entertainment (M&E) industries, AI is a driving force behind productivity.

Autodesk University Mainstage

Data, data, data

AI-driven productivity is customized to the specific needs of each industry, with various generative and multimodal AI models providing solutions for tasks like workflow optimization, manufacturing quality assurance, regulatory compliance, and design visualization. However, the value of AI across these sectors—and beyond—is determined by the quantity and quality of data used.

Quantity matters because more data enables AI systems to learn and improve. Quality, however, is more complex. It requires obtaining relevant data, ensuring data accuracy, and verifying that data usage complies with ethical standards.

Dell demonstrated how their workstations can run generative AI 3D content with NVIDIA Edify trained on Shutterstock ethically sourced content.

Customers are key

The importance of AI and data was evident throughout AU, with presentations and product demonstrations highlighting customer-centric solutions. Hardware providers showcased high-performance workstations equipped with powerful processors, optimized to support AI workloads. Meanwhile, data and asset management providers emphasized AI’s capabilities in extracting valuable information, managing metadata, and monitoring workflows in real time.

Lenovo displayed next generation workstations designed with a customer-centric focus. Partnering with Intel®, NVIDIA and Aston Martin, they offer high-end graphics, memory and processing power.

Autodesk’s Design & Manufacturing team showcased how they are addressing their customers' needs by delivering AI-powered efficiencies through Fusion, a cloud-based, unified platform for design and manufacturing. They also introduced Project Bernini, a research initiative focused on a new approach to generative AI. Unlike many generative models that prioritize the appearance of 3D objects, Bernini emphasizes generating structural 3D data from the inside out, which aligns closely with real-world production needs.

Experimental research project Bernini model generates shape and texture separately and does not confuse or meld those variables.

AI provides a wide range of possibilities based on industry-specific needs. For entertainment and gaming, AI can generate lifelike product and character models, while in manufacturing and design, it lays the foundation for real-world production. With tools like Fusion and research projects like Bernini, Autodesk aims to empower creators and makers to leverage AI for both efficiency and innovation.

We all know that the past year has driven innovation throughout the economy. As business conditions shifted in early 2020, innovators across the globe were forced to use any means to keep their organization open. The digital transformation we have seen in our society over the past year is not going away anytime soon, so it’s important for leaders and visionaries...

The post 10 Ways to Automate Business Processes Using a Tool You Already Have: Microsoft Power Platform appeared first on Anders CPA.

Together, they empower organizations to unlock the full potential of their data and teams

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The post The future of software development Is automation first appeared on TECH Intelligence and is written by Aidan Cunniffe.

When it comes to choosing AI SEO tools, the sheer variety of options can be overwhelming. The popular ones may not always be the best fit for your specific SEO workflow and processes. There’s no one-size-fits-all solution for finding the perfect AI tool for SEO automation. The right choice depends on your unique objectives, budget,...

The post 8 Best SEO Tools for AI Automation appeared first on noupe.

In addition to our Leveler, Denoiser, and Adaptive 'Hi-Pass' Filter, we now release the missing equalization feature with the new Auphonic AutoEQ.
The AutoEQ automatically analyzes and optimizes the frequency spectrum of a voice recording, to remove sibilance (De-esser) and to create a clear, warm, and pleasant sound - listen to the audio examples below to get an idea about what it does.

Screenshot of manually adjusted example settings for the equalizer plug-in 'Pro-Q3' by fabfilter.

What is Equalization and why is it difficult?

Equalization (EQ) in audio recording and reproduction is the process of adjusting the volume of different frequency bands within a signal.
The following vocal EQ cheat sheet, published by Producer Hive, will give you a small impression, of what can be influenced by equalizing:

Vocal EQ Cheat Sheet by Producer Hive.

On the other hand, it is very easy to ruin a good voice recording with manual heavy-handed equalization, resulting in voices, that sound very sharp or muddy or even like the speaker had a blocked nose.
Besides the skill and experience of an audio engineer, manual adjustments of frequencies also require a very good and linear studio playback device. For example, performing manual equalization with strongly bass-heavy speakers would most likely lead to a very sharp, unpleasant listening experience using treble-heavy headphones.

For singletrack productions with more than one speaker, equalizing is also a very complex and time-consuming process, as every voice has its unique frequency spectrum and needs its own equalization. One could separate speakers with cuts or create a track envelope to fade from one speaker to another, however, any solution is a very tedious job, if you do it by hand.

That is where the Auphonic AutoEQ comes in! All those steps are now available in just one click!

How does the Auphonic AutoEQ work?

The Auphonic Web Service analyzes your audio content and classifies the audio file into small and meaningful segments like music, silence, different speakers, etc. to process every single segment with the best matching algorithms.
All our features like the Adaptive Leveler, Dynamic Denoising, Adaptive 'Hi-Pass' Filtering, and now the new AutoEQ filter option are built on top of this basic processing.

Using Auphonic AutoEQ, spectral EQ profiles are created for each speaker separately and permanently changing over time. The aim of those time-dependent EQ profiles is to create a constant, pleasant sound in the output file even if there are slightly changing voices in the record, for example, due to modified speaker-microphone positions.

Audio Examples

Here are two short audio examples, which demonstrate some features of our AutoEQ.
We recommend listening with headphones so you can hear all the details.

Example 1. Female Speaker with Background Music

In the following example (BCB: The Voices of Bainbridge Island) of a female narrator speaking while background music is playing, you can easily recognize quite sharp 'sss' sounds in the female voice. This sharpness in the female voice is removed by the so-called De-essing feature of the Auphonic AutoEQ, while the background music is not changed.

Example 2. Dialog of Male and Female Speakers

The next example (BCB: The Voices of Bainbridge Island) shows how the AutoEQ optimizes a singletrack record containing two speakers with different voice characteristics. Our AutoEQ algorithms analyze each voice separately and calculate the matching frequency adjustments to optimize the voice of every single speaker.

AutoEQ Beta Integration in the Auphonic Web Service

To use the Auphonic AutoEQ, you just have to create a production or preset as you are used to, toggle “Advanced Parameters” on the top right in the section “Audio Algorithms” and select “Voice AutoEQ” within “Filtering”:

For a first test period, the AutoEQ will only be available for Beta and paying users, to incorporate your feedback and finalize an optimized version.
If you are a free user but want to try Auphonic AutoEQ: please just ask for access!

Practical Tips

For best results using Auphonic AutoEQ, however, it is still necessary, that your audio content is of sufficiently good quality, as no equalizer can make up frequencies that are not there in the first place. Audio files with low bitrates often lack important frequencies, that cannot be recovered by equalizing. AutoEQ is just a feature to boost or cut individual frequency bands, not a bandwidth extension. For more information about required audio quality, see the former blog post: Audio File Formats and Bitrates for Podcasts.

Another important topic is the definition of the 'best result'. Equalizing is a very subjective task, that differs a lot depending on every personal opinion. So Auphonic AutoEQ is set up to follow quite conservative rules of equalizing and rather apply subtle tweaks and remove obvious problems, than support personal preferences. This also means, your record will experience no significant changes from Auphonic AutoEQ if it sounds reasonably OK or pretty good already.

Conclusion

Auphonic audio post production algorithms keep getting better and better in leaps and bounds lately, offering you new Beta Features: Beta Auphonic Denoiser, Beta Auphonic Speech Recognition, and Beta Auphonic AutoEQ.
Right now we are fine-tuning all our current Beta Features with high intensity to release a new upgraded version of our Auphonic Web Service as soon as possible.
Please watch this channel for further updates – soon to come.

If you have any feedback for us or want to become a Beta user, you are very welcome to comment directly in our production interface or to contact us via email!

Cutting audio can be a rather tedious task. It requires a decent amount of time and is quite repetitive. Often silence segments, like speech breaks or breathing pauses, make cutting audio necessary in the first place.

Photo by Daniel Schludi on Unsplash

We introduce our new automatic silence cutting feature! It will make your life easier by saving you the time you would normally require to cut silence segments in your recordings.

Why do you need to cut silence segments?

Silence segments occur in your audio recordings naturally. They can be due to short speech breaks or breathing pauses. It's also possible, that at the beginning of a recording equipment needs to be re-adjusted, which also may result in a few seconds of silence.

Usually, listeners do not want to hear silence segments. The reason is easy: silence is redundant filler content. Hence, silence segments need to be cut to achieve a high-quality listening experience. Locating the segments and cutting them manually is tedious though and takes a decent amount of time for longer recordings.

This is why we developed and released our own automatic silence cutting feature.

How do we cut silence for you?

Our automatic silence cutting algorithm detects and cuts silence segments reliably. All you need to do is to enable the algorithm in your production - no further settings are required! This is the easiest possible way for you to cut silence in an audio file.

How to enable silence cutting for your production.

In our audio processing system, silence cutting is a multi-stage algorithm. We use our sophisticated voice activity detection algorithms to detect what is useful content and what is silence. Then we cut the silence parts and crossfade the remaining audio segments to make sure no audio artifacts are introduced. When cutting we ensure that intended speech breaks, e.g. between two sentences, remain untouched.

When we cut any audio, we make sure that chapter marks and speech recognition transcripts are adapted accordingly. Additionally, when exporting to other external services we make sure the cut tracks are exported and correctly labeled.

Audio Examples

Let's listen to two audio examples.

Example 1 (Singletrack production)

Here we have a singletrack production with three silence segments. The segments are located at the beginning of recording, between the first and the second "Hi" as well as at the end. Each silence segment is cut down to a length which still sounds natural, but does not annoy the listener.

The breathing onsets, which happen straight before voice kicks in again, are not cut. This makes sure that the character of the recording remains natural.

For this example, we also activated our new AutoEQ feature to remove pops in the audio.

Here is also a screenshot of how the result looks in our audio player. The gray areas show where our audio processing system cut the silence.

The result of example 01, as displayed by our Audio Inspector.

Thx to the Feel Free to Deviate podcast for providing this recording.

Example 2 (Multitrack production)

Let's also look at silence cutting in a multitrack production. This small excerpt of the TVEye podcast contains a music track and three speaker tracks. Before the intro music kicks in, there are a few seconds of silence.

The few seconds of silence at the beginning of the file are cut. After that, no further cuts are applied though to make sure the background music remains intact.

This is how the result of example 02 looks in our Audio Inspector.

Silence Cutting in our Audio Inspector

As you may have seen already in the examples, the cut segments are displayed as checked grey areas in the Audio Inspector. This is how they will be displayed by default.

Cut segments are displayed as checked gray areas by default.

During playback, the Audio Inspector will automatically skip these cut segments on the master track and play the silence segments if you activate the input track. This way, you can check each segment that was cut.

It's also possible to hide the cut segments. The following picture shows you how to do that.

You can also hide cut segments.

First, you must click onto the "?" icon in the bottom right corner to show the Audio Inspector options. Then you must toggle the "Silence Cut Region" switch. After that, the cut regions are not displayed anymore, and also won't be played back on the input track anymore.

If you click onto "Show Stats", you can activate the audio processing statistics. These statistics show you how much of your audio was cut, as well as the resulting track length after the cuts were applied.

The processing statistics tell you more about how much audio was cut.

Conclusion

With our automatic silence cutting feature, we went one step further towards the perfect audio assistant. Audio editing has been a tedious-but-necessary task for a long time, but with our automatic silence cutting feature, we just made it easier for you!

Feel free to send us your feedback - how do you like our new feature? Also, make sure to follow us, as we will release more automatic cutting algorithms for you in the future!

We're thrilled to introduce our Automatic Shownotes and Chapters feature. This AI-powered tool effortlessly generates concise summaries, intuitive chapter timestamps and relevant keywords for your podcasts, audio and video files.
See our Examples and the How To section below for details.

Why do I need Shownotes and Chapters?

In addition to links and other information, shownotes contain short summaries of the main topics of your episode, and inserted chapter marks allow you to timestamp sections with different topics of a podcast or video. This makes your content more accessible and user-friendly, enabeling listeners to quickly navigate to specific sections of the episode or find a previous episode to brush up on a particular topic.

Shownotes are also very likely to boost your show's Search Engine Optimization and eventually its popularity, leading to an increase in listeners.

However, especially structuring the content and finding useful positions for chapter marks is a very time-consuming process, that can be fully automated with our new feature.

Besides the obvious use of creating shownotes and chapters for podcasts, you can also use our new feature to easily generate an abstract of your lecture recording, take the summary of your show as the starting point for a social media post, or choose your favourite chapter title as the podcast name.

What happens behind the Scenes?

When the Automatic Shownotes and Chapters feature is selected, the first step is speech transcription by either our internal Auphonic Whisper ASR or any integrated External ASR Service of your choice.

Some open source tools and ChatGPT will then summarize the ASR resulting text in different levels of detail, analyze the content to identify sections with the different topics discussed, and finally complete each section with timestamps for easy navigation.
Beginning with the generation of a Long Summary, the number of characters is further reduced for a Brief Summary and from the brief summary a Subtitle and some Keywords for the main topics are extracted.

Depending on the duration of the input audio or video file, the level of detail of the thematic sections is also slightly adjusted, resulting in a reasonable number of chapters for very short 5-minute audio files as well as for long 180-minute audio files.

How to automatically generate Shownotes and Chapters in Auphonic

If you are a paying or beta user, you can automatically generate shownotes and chapters by checking the Automatic Shownotes and Chapters Checkbox in the Auphonic singletrack or multitrack Production Form with any of our ASR Services enabled.
Once your production is done, the generated data will show up in your transcript result files and in the well-known Auphonic Transcript Editor above the speech recognition transcript section.
By clicking on a chapter title in the Chapters section of the transcript editor, you can jump directly to that chapter in your transcript to review and edit that section.

Unless you have manually entered content before, the generated data will also be automatically stored in your audio files' metadata as follows:

• Generated Long Summary stored in metadata field Summary.
• Generated Subtitle stored in metadata field Subtitle.
• Generated Keywords stored in metadata field Tags.
• Generated Timestamps for thematic sections stored as Start Time of Chapters Marks.
• Generated Headlines for thematic sections stored as Chapter Title of Chapters Marks.

Please note that not all of our supported Output File Formats are designed to use metadata.
For details see our previous blog posts: ID3 Tags Metadata (used in MP3 output files), Vorbis Comment Metadata (used in FLAC, Opus and Ogg Vorbis output files) and MPEG-4 iTunes-style Metadata (used in AAC, M4A/M4B/MP4 and ALAC output files).

Example

As a real-life example, we automatically generated shownotes and chapters for the Lex Fridman Podcast #367: "Sam Altman: OpenAI CEO on GPT-4, ChatGPT, and the Future of AI".

Check out our transcript and generated shownotes:
LexFridmanPodcast367-transcript.html

Conclusion

The automatic generation of shownotes and chapters is a huge time-saver for podcasters and video creators, as it speeds up the tedious process of manually structuring and summarizing your content.

For now it is available for all paying or beta users. If you would like to become a beta user, or have any questions or feedback, please do not hesitate to contact us!

We all know the problem: the content is perfectly prepared, and everything is in place, but the moment you hit the record button, your brain freezes, and what pops out of your mouth is a rain of “ums”, “uhs”, and “mhs” that no listener would enjoy.
Cleaning up a record like that by manually cutting out every single filler word is a painstaking task.

So we heard your requests to automate the filler word removing task, started implementing it, and are now very happy to release our new Automatic Filler Cutter feature. See our Audio Examples and Usage Instructions below.

What is removed?

While the definition of filler words is not the same, depending on who you ask, some words can be used as filler as well as content. For example, “like”, “well”, “you know”, etc. cannot be removed without the risk of removing also content and destroying sentences, even if those words are used as filler words in some cases.

Therefore, we decided to focus on the removal of the obvious fillers, namely any kind of “ums”, “uhs”, “mhs”, German “ähm”, “äh”, “öh”, French “euh”, “euhm” and similar.

Audio Examples

1. English Male Speaker

The first audio example is an excerpt from the interview “From Racing Failure to Red Bull Champion: The Untold Christian Horner Story”. Our algorithm found and removed a remarkable ten filler words in this 45-second snippet:

Screenshot of the Auphonic Audio Inspector: each pale red shaded area corresponds to a cut-out filler word.

2. Austrian-German Female Speaker

The following example is an interview with the Austrian Ex-Foreign Minister, Karin Kneissl, who uses seven filler words within 26 seconds:

Usage Instructions

To use the Auphonic Automatic Filler Cutter feature, you just have to create a production or preset as you are used to and select “Cut Fillers” for “Automatic Cutting” in the section “Audio Algorithms”:

When your production is done, all cut-out filler words will appear as pale red shaded areas in the Auphonic Audio Inspector on the production status page, as you can see in the upper screenshot of the Audio Inspector.

If you want to remove silent segments from your audio as well, please also enable our Automatic Silence Cutting feature.

NOTE: Our Automatic Cutting features (for filler and silence) are not available for video files!

Behind the Scenes

For the training of our Automatic Filler Cutter AI-Algorithm, we created datasets that contain manually labeled audio files, collected from 'real world' audio data. So far, we have labeled, trained, and tested the system with English, German, Spanish, and French data.

However, in the Auphonic Web Service, you can activate and test the Automatic Filler Word Cutter for all languages. We would be very happy to hear how the filler removal works out for completely different-sounding languages from, e.g., the Asian, African, or Slavic language families.

Please send us feedback on any problems or error patterns you discover! This will help us generate specific data for the training to improve the algorithm and eliminate your problems.

Conclusion

Automatic filler word cutting is a powerful tool for podcasters looking to enhance the quality of their content. It boosts clarity and professionalism, all while making your editing process more efficient. Some users, however, see a touch of authenticity in filler words within podcasts. So, we leave it up to you to enable or disable the Automatic Filler Cutter feature for your next Auphonic production, depending on your desired style.

We are currently working on filler word cutting optimizations for more languages, so watch our channels to get all the news on our upgrades!

If you have any feedback for us – how the filler cutter is working in your language, what you do or don't like, what you miss, what else you would want to remove from your audio besides silence and filler words, etc. – you are welcome to contact us via email or directly comment on our production interface!

Building financial tools has never been more accessible. With the rise of web technologies, developers can create powerful market analyzers that are quick, responsive, and packed with features that were previously only available to large trading platforms. Here, we outline [...]

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The post Leveraging Automated Market Analyzers: A Developer’s Guide to Building Financial Tools with Web Technologies first appeared on CSS Reset.

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