Ultimate Ears Wonderboom 2: A Bombproof Bluetooth Speaker

Portable Bluetooth speakers have become ubiquitous, and none are more recognizable than the popular Ultimate Ears (UE) Boom range.

The UE Wonderboom 2 is an upgrade on the original Wonderboom we thoroughly reviewed in 2018. The older version has excellent sound, battery life, and is nigh on indestructible.

This ruggedness seems to be front and center on the upgraded model. It retains its IP67 dustproof/waterproof rating even when the flap protecting the charging port is left open.

The Wonderboom 2 might seem like a great case of “if it ain’t broke, don’t fix it.” The $99.99 price hasn’t changed and it looks very similar to the original Wonderboom.

Despite this, there are a few notable upgrades. The Wonderboom 2 is slightly larger than the original measuring 10.4 cm high with a 9.5 cm diameter. This is to house an upgraded battery claiming up to 13 hours playtime—30% more than it’s predecessor.

Another reason given for the larger frame was an upgrade to the speaker for better bass response. The punchy 360 sound of the original remains, with no distortion until you reach close to max volume.

A new addition is the Outdoor Boost mode, triggered using a button on the bottom. More than just a volume boost, it also equalizes the sound to take into account the lack of reflections from indoor walls.

UE Wonderboom 2 speakers can be paired to give true stereo. If you have an original Wonderboom, they can chain together—though you won’t get an actual stereo experience.

A notable omission for some might be the charging port. The Wonderboom 2 retains its microUSB port, and in the age where USB-C is king, this might be a mild annoyance.

In use, the Wonderboom 2 sounds excellent and feels as rugged as it looks. The IFA 2019 show floor had several models, along with a tank of water to dunk them in, and a dust tank to bury them in. We were also encouraged to drop them on the show floor while in use—in short, they were confident!

This speaker isn’t going toe-to-toe with any piece of home hi-fi gear, but then again it’s not designed for that. In our review of the original Wonderboom it survived being parked on by a car. The upgraded model looks every bit as tough.

If you are looking for a near unbreakable portable speaker under $100, the UE Wonderboom 2 is going to be tough to beat.

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Miele’s Smart Kitchen Appliances Work Far Better than You’d Imagine

Miele’s push towards total kitchen dominance is just getting started. The luxury appliances manufacturer recently demonstrated a trio of intelligent, connected appliances, through which it aims to take the stress out of cooking.

Miele’s latest induction hob works in conjunction with the app (available on both Android and iOS devices). This app issues instructions to the stovetop, which works in conjunction with you, the cook, and the on-board physical controls.

The premise is simple: recipes are provided through the app. There are only fifteen right now, but more are coming. When you’re ready to start cooking, the app turns the stovetop on. It heats the pan to the exact temperature required (so no more over or under cooking). It uses two temperature sensors to achieve this. The app knows how long to maintain the heat for. By following the instructions to season the dish and rotate the food, you end up with perfectly cooked, restaurant-quality food every time.

It’s possible to use the stove without the app at all, but both exist to complement each other. Buttons on the stove itself let you proceed to the next stage of the recipe, or start the timer.

The stove communicates with the hood. This hood looks like a device out of a science fiction movie. It contains a filter, extractor fan, and smart lighting. It’s also compatible with Alexa. The stove tells the hood what temperature it’s currently cooking at and the duration. The hood uses this information to intelligently enable or disable the extractor fan, along with periodically issuing bursts of fragrance.

The hood is also a work of art. It’s suspended from the ceiling through four attachment points, which also provide the mains power it needs to operate.

At IFA 2019 I experienced a live demonstration of Miele’s cook assist stove, app, and extractor hood, and it was remarkable. It all worked together seamlessly, and provided the experience we all think of when we consider technology. Of course the extractor fan should come on when you’re using the stove, yet few products actually exist to do this. Miele’s smart kitchen appliances are set to launch in April 2020, and we’re looking forward to a smart kitchen that is more than a gimmick.

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The 5 Best Audio Merger and Splitter Tools for MP3 Files

If you’ve never done it before, you should consider learning how to merge, join, combine, and split MP3 audio files. There are some nifty benefits to this, especially if you download your music.

I love Spotify and Pandora for streaming music, but there are good reasons to stick to downloading music. For example, you don’t own the music when you stream! Data usage is another big concern, which is why a lot of users still download YouTube videos as MP3s.

One big benefit of downloading music is that you can edit the files, whether to cut out extraneous bits (e.g. annoying intros) or mix a bunch of tracks into a personal mega-mix.

Interested? Here are the best free tools for merging and splitting audio files.

1. The Best Free Audio Editor: Audacity

Supported Platforms: Windows, Mac, Linux
Supported Formats: Any

Audacity is the go-to app if you’re looking for full control over the editing process. With it, you can split any bit of audio into as many pieces as you want, or you can merge as many audio files as you want in whatever order.

What’s nice is that you’ll also have access to all of Audacity’s other features, including audio filters and effects that may come in handy for music files that are problematic in some way. And the best part? Audacity skills can transfer to other endeavors, including these creative uses for Audacity.

Download: Audacity (Free)

Get started with these essential Audacity tips.

2. The Best Audio Merger Tool: MP3 Toolkit

Supported Platforms: Windows
Supported Formats: AAC, FLAC, MP3, OGG, WAV, WMA, and more

MP3 Toolkit is a collection of six separate audio manipulation tools that are all useful in some way: Converter, Ripper, Tag Editor, Merger, Cutter, and Recorder. For this article, we’re most interested in the Merger and Cutter.

The Merger lets you take multiple audio files, rearrange them in whatever order you want, then export it as a single combined audio file. The Cutter lets you take a single audio file, select a start and end time, then export that selection as a separate audio file.

You can download MP3 Toolkit for free, which will be marked as Unregistered. There’s no indication of whether this version limits features or has a time limit.

Download: MP3 Toolkit (Free, $30)

3. The Best Audio Splitter Tool: mp3DirectCut

Supported Platforms: Windows
Supported Formats: AAC, MP3

mp3DirectCut is a nifty little audio editor that’s lightweight and barebones: you can’t do much with it, but it’s very good at what it can do. Basic operations include cutting, copying, pasting, and recording audio.

What sets this app apart from most is the fact that it directly manipulates audio files without having to decompress them first. Not only does this result in a faster workflow, but it also preserves the original audio quality because it doesn’t need to be re-compressed.

Other notable features include ID3 tag editing, pause detection, batch file processing, auto-division of tracks by time value, and automatic filename and tag creation when splitting tracks.

Download: mp3DirectCut (Free)

4. Another Audio Splitter Tool: Mp3Splt

Supported Platforms: Windows, Mac, Linux
Supported Formats: FLAC, MP3, OGG

First things first, make sure you download Mp3Splt-GTK rather than simply Mp3Splt (which is a command line tool that’s more bother to learn than simply using the GTK version’s graphical interface).

Like mp3DirectCut, Mp3Splt can work on an audio file without having to decompress it first, resulting in a fast workflow and no impact to audio quality. This app is much simple though: you just pick a start and end time, then export that selection as a separate audio file.

If you have an entire album as a single audio file, Mp3Splt can auto-split using CUE files that mark where each track begins and ends. Auto-split using pause detection is available as well. Exported files can have their ID3 tags edited.

Download: Mp3Splt (Free)

5. Without Audio Merger Software? Command Line!

Supported Platforms: Windows, Mac, Linux
Supported Formats: Any

On Windows

One of the nifty things about Windows is that you can do a lot of cool stuff using the base command line utilities that come with the operating system. The copy command, for example, can actually merge MP3s into one.

Start by opening a Command Prompt window. You can do this by searching cmd in the Start Menu or by selecting Command Prompt in the Power Menu (keyboard shortcut Windows + X).

In the Command Prompt, navigate to the directory where your MP3s are stored. For me, that would be my Downloads folder:

cd C:\Users\Joel\Downloads

Then, use the following command:

copy /b file1.mp3 + file2.mp3 newfile.mp3

This basically takes the contents of file1.mp3 and file2.mp3 and combines them into a third file called newfile.mp3. You can have as many source files as you want as long as you separate them with a + sign, like so:

copy /b file1.mp3 + file2.mp3 + file3.mp3 + file4.mp3 newfile.mp3

The downside to this method is that it’s a literal concatenation of files, so the ID3 tags for all source MP3s except the first will be lost somewhere in the middle of the resulting MP3. Also check these essential CMD commands and these tips for improving Command Prompt.

On Linux and Mac

On Linux and Mac, you can use this command instead:

cat file1.mp3 file2.mp3 file3.mp3 > newfile.mp3

Other Tips for Managing Your Music

After splitting and merging a bunch of MP3s, you might find that you have a bit of a mess on your hands. Music management can be frustrating, especially if you’re fussy about file names and proper organization.

In that case, check out these music management tools. They provide easy ways to do things like mass renaming of files according to a pattern, batch editing of ID3 tags, etc.

Go even further with the best free audio editing software.

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Cryptic Wifi Networks

Cryptic Wifi Networks

Announcing Two New Natural Language Dialog Datasets

Posted by Bill Byrne and Filip Radlinski, Research Scientists, Google Research

Today’s digital assistants are expected to complete tasks and return personalized results across many subjects, such as movie listings, restaurant reservations and travel plans. However, despite tremendous progress in recent years, they have not yet reached human-level understanding. This is due, in part, to the lack of quality training data that accurately reflects the way people express their needs and preferences to a digital assistant. This is because the limitations of such systems bias what we say—we want to be understood, and so tailor our words to what we expect a digital assistant to understand. In other words, the conversations we might observe with today’s digital assistants don’t reach the level of dialog complexity we need to model human-level understanding.

To address this, we’re releasing the Coached Conversational Preference Elicitation (CCPE) and Taskmaster-1 dialog datasets. Both collections make use of a Wizard-of-Oz platform that pairs two people who engage in spoken conversations, just like those one might like to have with a truly effective digital assistant. For both datasets, an in-house Wizard-of-Oz interface was designed to uniquely mimic today’s speech-based digital assistants, preserving the characteristics of spoken dialog in the context of an automated system. Since the human “assistants” understand exactly what the user asks, as any person would, we are able to capture how users would actually express themselves to a “perfect” digital assistant, so that we can continue to improve such systems. Full details of the CCPE dataset are described in our research paper to be published at the 2019 Annual Conference of the Special Interest Group on Discourse and Dialogue, and the Taskmaster-1 dataset is described in detail in a research paper to appear at the 2019 Conference on Empirical Methods in Natural Language Processing.

Preference Elicitation
In the movie-oriented CCPE dataset, individuals posing as a user speak into a microphone and the audio is played directly to the person posing as a digital assistant. The “assistant” types out their response, which is in turn played to the user via text-to-speech. These 2-person dialogs naturally include disfluencies and errors that happen spontaneously between the two parties that are difficult to replicate using synthesized dialog. This creates a collection of natural, yet structured, conversations about people’s movie preferences.

Among the insights into this dataset, we find that the ways in which people describe their preferences are amazingly rich. This dataset is the first to characterize that richness at scale. We also find that preferences do not always match the way digital assistants, or for that matter recommendation sites, characterize options. To put it another way, the filters on your favorite movie website or service probably don’t match the language you would use in describing the sorts of movies that you like when seeking a recommendation from a person.

Task-Oriented Dialog
The Taskmaster-1 dataset makes use of both the methodology described above as well as a one-person, written technique to increase the corpus size and speaker diversity—about 7.7k written “self-dialog” entries and ~5.5k 2-person, spoken dialogs. For written dialogs, we engaged people to create the full conversation themselves based on scenarios outlined for each task, thereby playing roles of both the user and assistant. So, while the spoken dialogs more closely reflect conversational language, written dialogs are both appropriately rich and complex, yet are cheaper and easier to collect. The dataset is based on one of six tasks: ordering pizza, creating auto repair appointments, setting up rides for hire, ordering movie tickets, ordering coffee drinks and making restaurant reservations.

This dataset also uses a simple annotation schema that provides sufficient grounding for the data, while making it easy for workers to apply labels to the dialog consistently. As compared to traditional, detailed strategies that make robust agreement among workers difficult, we focus solely on API arguments for each type of conversation, meaning just the variables required to execute the transaction. For example, in a dialog about scheduling a rideshare, we label the “to” and “from” locations along with the car type (economy, luxury, pool, etc.). For movie tickets, we label the movie name, theater, time, number of tickets, and sometimes the screening type (e.g., 3D or standard). A complete list of labels is included with the corpus release.

It is our hope that these datasets will be useful to the research community for experimentation and analysis in both dialog systems and conversational recommendation.

Acknowledgements
We would like to thank our co-authors and collaborators whose hard work and insights made the release of these datasets possible: Karthik Krishnamoorthi, Krisztian Balog, Chinnadhurai Sankar, Arvind Neelakantan, Amit Dubey, Kyu-Young Kim, Andy Cedilnik, Scott Roy, Muqthar Mohammed, Mohd Majeed, Ashwin Kakarla and Hadar Shemtov.