Trending February 2024 # Best Voice Recognition Apps For Your Smartphone # Suggested March 2024 # Top 5 Popular

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Voice-recognition software is nothing new. But put it on a smartphone, and it comes to life. All of the frustrations of trying to control your PC by voice–fiddling for a microphone, repeating yourself again and again, resisting the urge to relent and turn to your trusty keyboard–are eliminated when you use the same technology on your mobile phone.

Luckily, plenty of apps provide such access–and more. Here’s a roundup of some of today’s best voice-recognition apps for your phone.

Google Mobile Apps

Platform: Android, BlackBerry, iOS

Price: Free

Whatever platform you’re on, you can give your fingers a rest with one of Google’s Mobile Apps. Whether you use the Google Quick Search Box on Android, the Google Mobile app for the iPhone, or the Google Search App for BlackBerry, the application lets you access the goodness of Google with the power of your voice. Search the Web, your contacts, and more without lifting a finger.

Bing

Platform: Android, iOS

Google isn’t the only search engine in town–and it isn’t the only one that delivers mobile searches powered by voice, either. Microsoft’s Bing does it, too, with the same elegance that the desktop Decision Engine delivers.

Vlingo

Platform: Android, BlackBerry, iOS, Nokia, Windows Phone

Price: Free (Basic version only; prices of Plus versions vary by platform)

Personal assistants are not just for the rich and famous: In virtual form, they’re also available to smartphone users. With versions for Android, iOS, and other platforms, the Vlingo app does your bidding, whether you want it to update your Facebook status, fire off a text message, or search the Web.

Siri Assistant

Platform: iOS

Price: Free

Too busy to type? Turn to Siri Assistant, a virtual personal assistant that resembles Vlingo but is more focused on such tasks as finding restaurants, making reservations, locating theater tickets, and booking taxis. And just like a human assistant, Siri Assistant learns more about your personal preferences over time.

DriveSafe.ly Pro

Platform: Android, BlackBerry, iOS

Price: $13.95 per year

Dragon Downloadable Apps

Platform: Android, BlackBerry, iOS

Price: Varies

Nuance’s popular line of Dragon voice-recognition software has gone mobile. The company offers various mobile apps, including Dragon Dictation (free), which lets you control many functions of your iOS device by voice; Dragon for Email (free), which enables BlackBerry users to compose and send messages by voice; and FlexT9 for Android ($4.99), which lets you choose your preferred method of input–speak, trace, write, or tap–based on your current needs.

ChaCha Answers

Platform: Android

Price: Free

Search engines are great when you’re looking for a wide swath of information. But if you have a narrowly specific question, digging through multiple pages of results looking for a simple answer is counterproductive. That’s when you need ChaCha Answers, a question-and-answer service that provides quick answers to your pressing questions.

Jibbigo Voice Translation

Platform: Android, iOS

Price: $4.99 and up

What good is talking if your intended audience can’t understand what your saying? Enter Jibbigo. This two-way translation app for Android and iOS listens as you talk in one language, and then translates your words into another language. It’s available in eight different language pairs, including English, Filipino, French, German, Japanese, Korean, Mandarin Chinese, and Spanish.

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The Best Voice Changer Apps For Android In 2023

The Best Voice Changer Apps For Android in 2023

Also Read: Cool And Harmless WhatsApp Pranks

Best Voice Changer Apps During Call For Android 1. Voice changer with effects by Baviux

On number one we have Voice Changer with effects by Baviux. The Android application holds strong potential to help users change their voice and have fun listening to the modified version. Using this best voice modulation app, you can change the recorded voice in more than 40 effects.

Highlighted Features: Voice changer with effects by Baviux

You can enjoy a plethora of effects with this application, ranging from Helium, Robot Monster, Zombie, Alien, Squirrel & so on.

Once you are done modulating the voice, you can share the audio file via WhatsApp, Email and on other platforms.

Unlike most of the best voice changing applications for Android, it supports importing pre-recorded sounds or further editing.

You can set the new voice file as ringtone or notification sound as well.

Get this Tool Here

2. Best Voice Changer – MeiHillMan

Highlighted Features :

Record or choose an existing file to play a prank on your family or friends.

Additionally, sharing is easy with this voice changer for Android. Share funny voice on Whatsapp, Twitter, Line, and Facebook.

Select from Drunk, Hexafluoride, God of Death, Robot, Chorus, The Smurfs, Diving, Bathroom, Old Radio, Helium, Bee, Astronaut, Monster, Squirrel, Fan, Kid, Cave, and Alien voices to play mischievous act on your friends or families.

Get this amazing tool from here

Also Read- Best Visual Voicemail Apps for Android and iOS

3. MagicCall – Voice Changer App

This is another good example of a voice changer app for phone calls. This smart app takes 36 MB space and requires a 5.0 or upper Android version to run. Additionally, this app claims as a voice changer during a call in real-time.

Highlighted Features :

You can easily swap voices during the call.

Moreover, with the help of this voice changer app for Android, you can choose from various background effects such as music Raining noise, Concert, Happy Birthday Background, and Traffic Noise.

And yes, if you are looking for a voice changer male to female, you are at the right place. Download this Magic Call, and start prank on your friends.

Download this voice changer for Android from here

4. Voice Changer by AndroidRock

As the name suggests itself, this app as a voice changer for Android is there for you to allot so many funny experiences. This app takes 6.3 MB, and requires 4.1 Android and above. There is a simple and funny UI, which will help you to convert your voice in a more fun way.

Highlighted Features:

Unlike others, this app helps you to record and enjoy the voices from different effects such as Twisted tongue, Creepy movie, Foreigner and so on.

Can edit and view the saved audio files in this app.

A great source of entertainment to share your funny voices with your friends through social media platforms and yes, even with Bluetooth.

Get This Amazing App Here

5. Funcalls – Voice Changer & Call Recording

Here come the Funcalls, the voice changer app for phone calls. You can play a prank on anyone with the help of this app. This app weighs 25 MB and requires a 4.0 Android version and more.

Highlighted Features:

You can use various sound effects during the call.

Furthermore, you can even record the calls and share them with your friends and families.

Additionally, international phone calls in more than 150 countries are possible with this voice changer app for phone calls.

6. Voice Changer Voice Recorder – Editor & Effect

Designed & developed by Handy Tools Studio, Voice Changer is an ultimate option if you want to change your voice to a smooth & appealing one to sing songs. The application is a great choice if you want to become a super voice maker. The Android app is best-known for improving voice & altering it with funny sound effects.

Highlighted Features: Voice Changer Voice Recorder – Editor & Effect

Support voice effects such as Alien, Robot, Bee, Device, Female, Child, Chipmunk, Old Man and so on, which makes it the best voice changing app for Android.

Additionally, you can enjoy voice effects for singing, including Karaoke Reverb, Studio Reverb, Theatre Reverb, Concert Reverb and more.

The voice changer app is even compatible with superhero voice effects: for Transformers, Star War, Minions and more.

Get This Amazing App Here

7. VoiceFX – Voice Changer with voice effects

Installed by Millions of users worldwide, VoiceFX is undoubtedly a powerful voice recording and changing application for Android users. You can add and tweak your voice with music and audio effects. It even gives users the ability to live playback your voice with added effects.

Highlighted Features: VoiceFX – Voice Changer with voice effects

Using this best voice changer app for Android you can change voice using the effects such as Chipmunk, Male, Female, Robot, Monster, Sheep, Kid, and more.

With the built-in sound recorder, users can record their voices and apply multiple audio effects until they get a unique voice for themselves.

You can save the new voice files as MPR and share it on Social Media.

Get this App Here

8. Voice Changer Prank Calls

Last, but not the least in consideration, Voice Changer Prank Calls ins an amazing audio modulation application for Android that allows you to change your voice and make free prank calls to anyone you wish. You can instantly change voice and preview the new one before calling someone.

Highlighted Features: Voice Changer Prank Calls

The voice changer app works well even without WIFI.

Using this Android application, you can alter your voice as many times as you want during the call.

Offers a vast selection of voice effects.

Comes with a quick voice modification process!

Get This App Here

9. Change My Voice

Next on our list of Top 10 Voice Changing Applications for Android (2023) is Change My Voice. It presents a straightforward UI and easy to navigate buttons. To use the application, all you have to do is: Install the voice modulator app from Google Play Store, record yourself in real-time and change the voice with multiple effects.

Highlighted Features: Change My Voice

Simple & easy to use voice changing application for Android users.

Offers multiple tools and sliders to create voice medications. You can even alter the pitch and speed of your voice.

What makes Change My Voice the Best Sound Changing App? It’s their ability to control numerous voice changing options, adding echo and so on.

10. Scary Voice Changer – Horror Sounds Voice Recorder

Modulating voices in scary & horror audios is indeed a straightforward and fun process with Scary Voice Changer app for Android. It just needs a few taps and you can modulate the voice without any hassles. The speech changing application allows you to record yourself for free and add horror noises to scare people.

Highlighted Features:

The voice changer app is best known for its brilliant set of sound effects for editing.

The built-in audio editor is straightforward and easy to use. Suitable for both novice and pro smartphone users.

Once you are satisfied by modulating the voices, you can share the audio files directly with your friends within the app.

Final Words

This new year prank anyone with voice changer apps. These above-mentioned funny apps can become your partner in crime in mischievous acts. Don’t troll much to anyone!

We‘re Listening!

Recommended Readings:

Best Compass Apps For Android and iOS

How to Change Google Assistant Voice On Android

Best 10 Free Android Apps for VOIP & VIDEO Calling

Quick Reaction:

About the author

Harshita Singh

Implementing Lstm For Human Activity Recognition Using Smartphone Accelerometer Data

This article was published as a part of the Data Science Blogathon

Photo by kevin laminto on Unsplash

sophisticated sensors such as accelerometers and gyroscopes. Accelerometers embedded in smartphones are used to detect the orientation of the phone. The gyroscope (also called gyro in short) tracks the rotation or twist which adds an additional dimension to the information provided by the accelerometer. While the accelerometer measures the linear acceleration, the gyro on the other hand measures the rotational angular velocity. The information provided by these sensors supplements each other in the process of motion sensing.

There are several publications, research articles, and blogs wherein the authors have used machine learning-based techniques for HAR. The performance of such methods largely depends on the quality of the engineered features. Handcrafting features for any specific application require excellent domain knowledge. Besides feature engineering is quite a tedious task and without domain understanding, you may not be able to obtain state-of-the-art (SOTA) results. This is where deep learning comes to our rescue!

In this article, we are going to employ Long short-term memory (LSTM), an artificial recurrent neural network architecture for the human activity recognition task, which will learn complex features automatically from the raw accelerometer signal to be able to differentiate between common human activities.

About data

Smartphones contain tri-axial accelerometers that measure acceleration in all three spatial dimensions. In this article, we shall be using the raw accelerometer signal data sourced from WISDM Lab, Department of Computer & Information Science, Fordham University, NY (Link to the dataset). Note that the file is the raw data file — WISDM_ar_v1.1_raw.txt

This data is collected from 36 different users as they performed some day-to-day human activities such as — walking, sitting, standing, jogging, ascending and descending stairs for a specific period of time. In all cases, data is collected at a frequency of 20 samples per second, that is one record every 50 milliseconds.

‘user’ denotes the user ID,  ‘timestamp’ is the Unix timestamp in nanoseconds, and the rest are the accelerometer readings along the x, y, and z axes/dimensions at a given instance of time. Our target variable(class-label) is ‘activity’ which we intend to predict.

Here is a glimpse of our dataset –

Before we start training our models, the dataset needs to be clean and organized.

Data Cleaning & Preprocessing

drop null values.

change datatype of the ‘z-axis’ column to float.

drop the rows where the timestamp = 0.

sort data in ascending order of ‘user’ and ‘timestamp’ columns.

# importing libraries %matplotlib inline import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') # reading raw data file columns = ['user','activity','timestamp', 'x-axis', 'y-axis', 'z-axis'] df_har = pd.read_csv('WISDM_ar_v1.1/WISDM_ar_v1.1_raw.txt', header = None, names = columns) # removing null values df_har = df_har.dropna() df_har.shape # transforming the z-axis to float df_har['z-axis'] = df_har['z-axis'].str.replace(';', '') df_har['z-axis'] = df_har['z-axis'].apply(lambda x:float(x)) # drop rows where timestamp is 0 df = df_har[df_har['timestamp'] != 0] # arrange data in ascending order of user and timestamp df = df.sort_values(by = ['user', 'timestamp'], ignore_index=True)

Here is how our data looks like post-cleaning and sorting —

Now we are left with 1085360 rows.

Exploratory Data Analysis

Analyzing class label distribution —

sns.set_style(“whitegrid”) sns.countplot(x = ‘activity’, data = df) plt.title(‘Number of samples by activity’) plt.show()

It can be seen, there is a significant class imbalance here with the majority of the samples having class-label ‘Walking’ and ‘Jogging’. ‘Sitting’ and ‘Standing’ activities have the least representation in the dataset.

Now let’s see how the individual users are contributing to each activity.

sns.countplot(x = ‘user’,  hue = ‘activity’, data = df) plt.title(‘Activities by Users’) plt.show()

 

It can be observed that not all the users are performing all the activities. The time for which they perform each activity is also disproportionate. Regardless, this won’t be affecting us as we have a sufficiently large number of data samples, and it is assumed that all the users are alike.

We can also go a step ahead and visualize how the signal values in each of the x, y, and z dimensions vary with time.

activities = [‘Walking’, ‘Jogging’, ‘Upstairs’, ‘Downstairs’, ‘Sitting’, ‘Standing’]: for i in activities: data36 = df[(df[‘user’] == 36) & (df[‘activity’] == i)][:400] sns.lineplot(y = ‘x-axis’, x = ‘timestamp’, data = data36) sns.lineplot(y = ‘y-axis’, x = ‘timestamp’, data = data36) sns.lineplot(y = ‘z-axis’, x = ‘timestamp’, data = data36) plt.legend([‘x-axis’, ‘y-axis’, ‘z-axis’]) plt.ylabel(i) plt.title(i, fontsize = 15) plt.show()

For visualization, we have considered a subset of 400 samples. This is equivalent to 20 secs of activity (as the frequency of data collection was 20 Hz). As we notice, the signal shows periodic behavior for activities like Walking, Jogging, Upstairs, and Downstairs while it has the very little movement for stationary activities like Sitting and Standing. These signals can be modeled as time-series data.

This brings us to the heart of the article – training an LSTM network.

Preparing Data

The LSTM model expects fixed-length sequences as training data. We shall use a familiar method for generating these. Each generated sequence (or window) contains 50 records corresponding to 2.5 secs of activity (recall that the frequency of the data collection as mentioned in the source website is 20 Hz).

random_seed = 42 n_time_steps = 50  n_features = 3  step = 10  n_classes = 6  n_epochs = 50 batch_size = 1024 learning_rate = 0.0025 l2_loss = 0.0015 segments = [] labels = [] for i in range(0, df.shape[0]- n_time_steps, step): xs = df['x-axis'].values[i: i + 50] ys = df['y-axis'].values[i: i + 50] zs = df['z-axis'].values[i: i + 50] label = stats.mode(df['activity'][i: i + 50])[0][0] segments.append([xs, ys, zs]) labels.append(label) #reshape the segments which is (list of arrays) to a list reshaped_segments = np.asarray(segments, dtype= np.float32).reshape(-1, n_time_steps, n_features) labels = np.asarray(pd.get_dummies(labels), dtype = np.float32)

Note that here we have considered overlapping windows (with 80% overlap) of data. Because our activity is continuous, this overlap ensures that each subsequent window carries some information from the previous window.

At this stage, if you check the shape of the transformed data –

reshaped_segments.shape

(108531, 50, 3)

108531 sequences of 200 rows, each containing x, y, and z data. Our original raw dataset has drastically reduced size after the transformation. Note that the class label assigned to a sequence (window) is the activity that occurs most frequently in that window.

Splitting data into train and test sets –

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(reshaped_segments, labels, test_size = 0.2, random_state = random_seed) Building Model Architecture from keras.models import Sequential from keras.layers import LSTM, Dense, Flatten, Dropout model = Sequential() # RNN layer model.add(LSTM(units = 128, input_shape = (X_train.shape[1], X_train.shape[2]))) # Dropout layer model.add(Dropout(0.5)) # Dense layer with ReLu model.add(Dense(units = 64, activation='relu')) # Softmax layer model.add(Dense(y_train.shape[1], activation = 'softmax')) # Compile model

Let’s see the model summary –

model.summary()

As you can see, we have got more than 76k trainable parameters. Because of so many trainable parameters, the model tends to overfit easily. That is why a dropout layer is recommended to avoid overfitting.

Now comes the training part. We will train our model for 50 epochs and keep track of accuracy and error on the validation set.

Model Training & Evaluation history = model.fit(X_train, y_train, epochs = n_epochs, validation_split = 0.20, batch_size = batch_size, verbose = 1)

Plotting training and validation accuracy and cross-entropy loss against a number of epochs –

plt.plot(np.array(history.history['loss']), "r--", label = "Train loss") plt.plot(np.array(history.history['accuracy']), "g--", label = "Train accuracy") plt.plot(np.array(history.history['val_loss']), "r-", label = "Validation loss") plt.plot(np.array(history.history['val_accuracy']), "g-", label = "Validation accuracy") plt.title("Training session's progress over iterations") plt.legend(loc='lower left') plt.ylabel('Training Progress (Loss/Accuracy)') plt.xlabel('Training Epoch') plt.ylim(0) plt.show()

Our LSTM model seems to learn well with accuracy reaching above 96% and cross-entropy loss is well below 0.2 for both training and validation data.

Now let’s assess the performance of the trained model on the test dataset –

loss, accuracy = model.evaluate(X_test, y_test, batch_size = batch_size, verbose = 1) print("Test Accuracy :", accuracy) print("Test Loss :", loss) Output: Test Accuracy : 0.9619938135147095 Test Loss : 0.13234397768974304

Now let’s observe the confusion matrix to see how our model performs on each class label.

Confusion matrix predictions = model.predict(X_test) class_labels = ['Downstairs', 'Jogging', 'Sitting', 'Standing', 'Upstairs', 'Walking'] max_test = np.argmax(y_test, axis=1) max_predictions = np.argmax(predictions, axis=1) confusion_matrix = metrics.confusion_matrix(max_test, max_predictions) sns.heatmap(confusion_matrix, xticklabels = class_labels, yticklabels = class_labels, annot = True, linewidths = 0.1, fmt='d', cmap = 'YlGnBu') plt.title("Confusion matrix", fontsize = 15) plt.ylabel('True label') plt.xlabel('Predicted label') plt.show()

As it can be noticed from the confusion matrix, the two most common activities in our dataset i.e. Walking and Jogging are correctly classified with very high accuracy. Although Sitting and Standing are minority classes, yet our model is accurately able to differentiate them. The accuracy is not as high as the other classes for Upstairs and Downstairs activities. This is expected as these two are very similar activities so the underlying data may not be sufficient to accurately different them.

Conclusion

We started with the raw accelerometer signal data consisting of just 4 relevant features – reading of accelerometer along x, y, and z axes and the timestamp at which the readings were taken. We trained a simple LSTM network on the raw time-series data. LSTM model learns complex features automatically from the sequential data to be able to predict the class label with high accuracy.

By coupling the tri-axial accelerometer data with the data from the tri-axial gyroscope, it can be possible to distinguish between similar classes like Standing and Sitting, as well as recognize other activities with even higher accuracy.

References

Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore (2010). Activity Recognition using Cell Phone Accelerometers, Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data (at KDD-10), Washington DC.

Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21st European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning, ESANN 2013

 Human Activity Recognition using LSTMs on Android — TensorFlow for Hackers (Part VI) by Venelin Valkov.

Using Deep Learning for Mobile Health Data by Burak bhimmetoglu.

About the Author

The media shown in this article on LSTM for Human Activity Recognition are not owned by Analytics Vidhya and are used at the Author’s discretion.

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Play.ht is a leading AI voice generator that offers a free version with no watermarks or time limits. With chúng tôi you can generate AI voices in various languages, accents, and voices, making it a versatile tool for diverse applications. Its simple and intuitive interface ensures a seamless user experience, allowing you to create lifelike voices effortlessly.

Listnr is an exceptional AI text-to-speech voice generator that offers a free version. This tool enables users to convert text into speech with incredible accuracy, producing voices that closely resemble real human voices. Its user-friendly interface and extensive language and accent options make Listnr a reliable choice for various text-to-speech needs.

Murf.ai is a feature-rich AI voice generator that offers a free version along with a range of customization options. Whether you need a specific voice for your project or want to fine-tune the nuances of speech, chúng tôi has got you covered. It boasts realistic AI voices in 20 languages, making it one of the most popular and impressive voice generators available.

Lovo.ai is an award-winning AI-based voice generator and text-to-speech platform that offers a free trial version. This platform excels in producing voices that closely resemble real human voices, providing an immersive experience for various applications. From sales videos to animations, podcasts to TV commercials, chúng tôi caters to a wide range of use cases.

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Notevibes is a versatile AI voice generator that offers a free version, making it an excellent choice for individuals and professionals alike. It is trusted by large companies globally and supports over 150 countries with its multilingual capabilities. Notevibes’ online editor provides fantastic features like emphasis and volume control, pause customization, and speed and pitch adjustments.

1. Can I use AI voice generators for commercial purposes?

Yes, many AI voice generators, including the ones mentioned in this article, offer commercial usage options. Make sure to review the terms and conditions of each platform to ensure compliance with their licensing and usage policies.

2. Are the generated AI voices indistinguishable from real human voices?

3. Can I customize the generated AI voices?

Yes, most AI voice generators offer customization options to tailor the generated voices according to your preferences. You can often adjust parameters such as pitch, speed, emphasis, and even add pauses or breaths to make the voices sound more natural. Some platforms also allow you to modify the accent, tone, or style of the voice to suit your specific needs.

4. Are there any limitations or restrictions on the free versions?

While free versions of AI voice generators provide valuable functionality, they may come with certain limitations or restrictions compared to their paid counterparts. These limitations can include usage caps, reduced access to voice libraries, or watermarked output. It’s essential to review the details of each platform’s free version to understand any limitations and determine if they meet your requirements.

5. Can I use AI voice generators in my applications or products?

Yes, many AI voice generators allow you to integrate their services into your applications, products, or services through APIs (Application Programming Interfaces) or SDKs (Software Development Kits). This enables you to leverage the power of AI-generated voices within your own software or platforms, enhancing the user experience and adding value to your offerings.

6. How accurate are the translations for multilingual voice generation?

7. Are there any privacy concerns with using AI voice generators?

AI voice generators typically process the text inputs and may store the generated voices temporarily to improve their models or enhance user experience. It’s crucial to review the privacy policies of the platforms you choose and ensure they align with your privacy preferences. If privacy is a significant concern, look for platforms that prioritize data security and offer transparent privacy practices.

AI voice generators have made significant strides in delivering realistic and high-quality synthesized voices. Whether you need AI-generated voices for videos, podcasts, virtual assistants, or any other application, the tools mentioned in this article offer excellent options. chúng tôi Listnr, chúng tôi chúng tôi and Notevibes are all reliable choices, providing free versions or trials to help you experience their capabilities firsthand. Explore these AI voice generators, unleash your creativity, and enhance your projects with lifelike and engaging AI voices.

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How To Pick The Right Wireless Charger For Your Smartphone

We may earn revenue from the products available on this page and participate in affiliate programs. Learn more ›

The Mophie Charge Stream pad is designed to sit on a nightstand or desk. Mophie

Wireless charging is a wonderfully simple idea. You plop down your smartphone on top of a pad on your nightstand and electricity flows from the grid into your device so you can burn it up the next day scrolling through newsfeeds and watching social media stories. Smartphones have had wireless charging tech inside since 2024 (it was a Blackberry!), but last year, Apple shoved a wireless charging coil into its iPhone X and iPhone 8 devices, which dragged it from the realm of a “hey neat!” feature to a seemingly essential inclusion on the spec sheet of any high-end smartphone.

And while using a wireless charger is simple, picking the correct one can be surprisingly challenging. Variables like design, form factor, and power level can make a huge difference in the efficacy of your new power setup. Here are some of the key things to consider when making the jump to wireless power, as well as some insight into how the tech works in the first place.

How does it work?

A look inside the Belkin Powerhouse charger. Belkin

The most popular wireless charging technology comes from Qi (pronounced “chee”) and it involves electromagnetic coils in both the charging device and the gadget you want to charge. The power transfers to the device via induction, which will work through some smartphone cases if they’re thin enough and the material doesn’t create interference.

If you want a slightly deeper dive into exactly how the Qi technology works, you can check out the company’s description here.

What’s the deal with Watts?

This is where the shopping process gets a little tricky because the kind of phone you have and its power compatibility can make a profound difference on how fast and efficiently a wireless charger can power up your phone.

For reference, the wired charger that comes packed with the new Google Pixel 3, for example, is an 18-watt charger. Qi wireless charging technology as it currently stands can transfer up to 15 watts of power via wireless induction. Right now, the fastest-charging wireless phones can handle 10 watts, which typically translates to full recharge times in the roughly three-hour range, but that can vary.

Generally speaking, 10 watts is all you’ll need if you want to get the fastest possible wireless charge out of your device. That goes for phones like the Pixel 3 (which is the first Pixel to include wireless charging), the Samsung Galaxy devices, and the LG G7 ThinQ. Meanwhile, iPhones are currently capped at 7.5 watt wireless charging, but buying a future-proof charger is likely a better investment.

If you’re not sure what wattage is correct, it won’t harm your device to overshoot the number. The technology knows how much power is required—at least if you’re buying from a reputable company and dealer. If you buy something too weak, the charge will take much longer.

Choosing a design

There are two main types of wireless chargers out there. The first is a pad that lays flat on your table or desk, while the second is a stand that holds the phone at a semi-upright angle so you can see the screen without having to hover your head over the device.

The style you pick depends on your preference, though the location of your charging station will likely guide your decision. I prefer the stand-style chargers most of the time because picking up your phone off the pad stops them from charging and using a device that’s laying flat on a desk is awkward. If you’re clumsy or putting it on a nightstand, however, where you probably won’t be using it much during its juice-up, then a lay flat charger might be a better call since it reduces the chance you’ll knock it onto the floor in a groggy morning haze.

Choosing a brand

Sometimes, however, buying a first-party charger that’s the same brand as your smartphone does offer some benefits. The Pixel Stand, for instance, turns your phone into a makeshift alarm clock and digital photo frame.

Samsung’s duo charger can power up both a smartphone and a 7.5-watt at the same time, which is really handy if you use both every day.

What case can I use with my wireless charger?

Some companies, like Belkin, offer guidelines on their charger product pages to indicate what qualities might prevent a wireless charger case from allowing power to make its way in.

In general, though, thinner is better.

What about portable chargers?

Portable battery packs are essential kit for carrying your gadgets around, and there are wireless options out there, including some rather excellent options from Mophie. Wireless charging is a nice feature if you lose your cable or often forget to bring one (or you want to charge two gadgets at once and only have one cable). But, wireless charging is inefficient and slow, so portable wireless packs aren’t great at providing the quick bursts of charge you often need when you’re on the move.

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The best singing apps for Android

Price: Free

Dolby On is one of the best audio recording apps for Android. It does the same stuff as any audio (or voice recorder), but adds a few extras to make the experience even better. The app contains six audio filters to bring out some specific characteristics of your voice along with an EQ, an audio trimmer, and external microphone support. The export settings are a little wonky, but you can make some actually good recordings with this app. It has all of the tools you’d need as a vocalist for the low, low price of free.

See also: The best audio recording apps for Android

FL Studio

Price: $14.99 with in-app purchases

FL Studio escalates the audio recording capabilities to the next level. It’s a full studio app where you can record vocals, add beats, and create whole songs. The learning curve is rather steep, but it’s actually pretty fun once you get used to it. The app contains various presets, modules, synths, samplers, beats, and MIDI controller support. Thus, you can create entire songs with a multitude of tools at your disposal. The app is a bit expensive, but getting software like this on desktop computers can cost hundreds of dollars so it’s kind of a steal. The in-app purchases are for optional add-ons that don’t affect functionality.

Check out more DAWs here: The best musician apps for Android

Hi-Q MP3 Voice Recorder

Also try: The best voice recorder apps for Android

KaraFun

Try these too: The best karaoke apps for Android

The Metronome by Soundbrenner

Price: Free

The Metronome by Soundbrenner is one of the easiest and best singer apps. It’s a metronome where you can define a beat and then rap or sing to it. The app also has 20 sounds, a dark or light theme, and even support for MIDI devices. You can create various time signatures, save presets, and more as well. There are many other metronome apps and we have some linked up below the download button, but this one should hit all the notes (pun intended) for most people. It’s also entirely free to use.

Check these: The best metronome apps for Android

Nail the Pitch

Price: Free / $4.99

Joe Hindy / Android Authority

Nail the Pitch is one of the better singing apps for Android. It allows you to monitor the notes you sing so you can see what pitch they were. Some other functions include the ability to test your entire vocal range, record your voice for further analysis, and more. There isn’t much else to it than that. However, this app promises to help you practice pitch and it does. We like it when apps do what they say.

Riyaz

The Vocal Coach

Vocolo

Price: Free trial / $7.99 per month / $29.99 per year

Joe Hindy / Android Authority

Voloco is a recording studio specifically for singers. This is a lot like Dolby On, but with more complex things to help your recorded singing sound better. For instance, you can remove background noise, put your voice to a beat, EQ your voice, play with things like reverb and compression, and more. It also has the ability to extract vocals from existing songs and mess with them to create some weird stuff. Some other features include a notepad for jotting down lyrics, the ability to share your work with others on the site, and over 50 vocal effects. The monthly subscription is a tad expensive and you save a ton of money by going for the yearly version. Luckily, the app gives you a week to test it out before it makes you pay anything.

YouTube

Price: Free / $12.99 per month

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